Parameters

• images (ImageInput) —Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. Ifpassing in images with pixel values between 0 and 1, setdo_rescale=False.
• do_resize (bool,optional, defaults toself.do_resize) —Whether to resize the image.
• size (dict[str, int],optional, defaults toself.size) —Dictionary in the format{"height": h, "width": w} specifying the size of the output image afterresizing.
• resample (PILImageResampling filter,optional, defaults toself.resample) —PILImageResampling filter to use if resizing the image e.g.PILImageResampling.BILINEAR. Only hasan effect ifdo_resize is set toTrue.
• do_rescale (bool,optional, defaults toself.do_rescale) —Whether to rescale the image values between [0 - 1].
• 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) —Whether to normalize the image.
• image_mean (float orlist[float],optional, defaults toself.image_mean) —Image mean to use ifdo_normalize is set toTrue.
• image_std (float orlist[float],optional, defaults toself.image_std) —Image standard deviation to use ifdo_normalize is set toTrue.
• return_tensors (str orTensorType,optional) —The type of tensors to return. Can be one of:
  • Unset: Return a list ofnp.ndarray.
  • TensorType.TENSORFLOW or'tf': Return a batch of typetf.Tensor.
  • TensorType.PYTORCH or'pt': Return a batch of typetorch.Tensor.
  • TensorType.NUMPY or'np': Return a batch of typenp.ndarray.
  • TensorType.JAX or'jax': Return a batch of typejax.numpy.ndarray.
• data_format (ChannelDimension orstr,optional, defaults toChannelDimension.FIRST) —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.
  • Unset: Use the channel dimension format of the input image.
• input_data_format (ChannelDimension orstr,optional) —The channel dimension format for the input image. If unset, the channel dimension format is inferredfrom 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.
  • "none" orChannelDimension.NONE: image in (height, width) format.
• do_convert_rgb (bool,optional, defaults toself.do_convert_rgb) —Whether to convert the image to RGB.

Parameters

• images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]) —Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. Ifpassing in images with pixel values between 0 and 1, setdo_rescale=False.
• do_resize (bool,optional) —Whether to resize the image.
• size (dict[str, int],optional) —Describes the maximum input dimensions to the model.
• default_to_square (bool,optional) —Whether to default to a square image when resizing, if size is an int.
• resample (Union[PILImageResampling, F.InterpolationMode, NoneType]) —Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Onlyhas an effect ifdo_resize is set toTrue.
• do_center_crop (bool,optional) —Whether to center crop the image.
• crop_size (dict[str, int],optional) —Size of the output image after applyingcenter_crop.
• do_rescale (bool,optional) —Whether to rescale the image.
• rescale_factor (Union[int, float, NoneType]) —Rescale factor to rescale the image by ifdo_rescale is set toTrue.
• do_normalize (bool,optional) —Whether to normalize the image.
• image_mean (Union[float, list[float], NoneType]) —Image mean to use for normalization. Only has an effect ifdo_normalize is set toTrue.
• image_std (Union[float, list[float], NoneType]) —Image standard deviation to use for normalization. Only has an effect ifdo_normalize is set toTrue.
• do_convert_rgb (bool,optional) —Whether to convert the image to RGB.
• return_tensors (Union[str, ~utils.generic.TensorType, NoneType]) —Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
• data_format (~image_utils.ChannelDimension,optional) —OnlyChannelDimension.FIRST is supported. Added for compatibility with slow processors.
• input_data_format (Union[str, ~image_utils.ChannelDimension, NoneType]) —The channel dimension format for the input image. If unset, the channel dimension format is inferredfrom 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.
  • "none" orChannelDimension.NONE: image in (height, width) format.
• device (torch.device,optional) —The device to process the images on. If unset, the device is inferred from the input images.
• disable_grouping (bool,optional) —Whether to disable grouping of images by size to process them individually and not in batches.If None, will be set to True if the images are on CPU, and False otherwise. This choice is based onempirical observations, as detailed here:https://github.com/huggingface/transformers/pull/38157

• data (dict) — Dictionary of lists/arrays/tensors returned by thecall method (‘pixel_values’, etc.).
• tensor_type (Union[None, str, TensorType],optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors atinitialization.

Parameters

• pixel_values (torch.Tensor of shape(batch_size, num_channels, image_size, image_size),optional) —The tensors corresponding to the input images. Pixel values can be obtained using{image_processor_class}. See{image_processor_class}.__call__ for details ({processor_class} uses{image_processor_class} for processing images).
• bool_masked_pos (torch.BoolTensor of shape(batch_size, num_patches),optional) —Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
• head_mask (torch.Tensor of shape(num_heads,) or(num_layers, num_heads),optional) —Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:
  • 1 indicates the head isnot masked,
  • 0 indicates the head ismasked.
• output_attentions (bool,optional) —Whether or not to return the attentions tensors of all attention layers. Seeattentions under returnedtensors for more detail.
• output_hidden_states (bool,optional) —Whether or not to return the hidden states of all layers. Seehidden_states under returned tensors formore detail.
• interpolate_pos_encoding (bool,optional) —Whether to interpolate the pre-trained position encodings.
• return_dict (bool,optional) —Whether or not to return aModelOutput instead of a plain tuple.

Atransformers.modeling_outputs.BaseModelOutputWithPooling or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (ViTConfig) 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.

• pooler_output (torch.FloatTensor of shape(batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processingthrough the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returnsthe classification token after processing through a linear layer and a tanh activation function. The linearlayer weights are trained from the next sentence prediction (classification) objective during pretraining.

• 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

• pixel_values (torch.Tensor of shape(batch_size, num_channels, image_size, image_size),optional) —The tensors corresponding to the input images. Pixel values can be obtained using{image_processor_class}. See{image_processor_class}.__call__ for details ({processor_class} uses{image_processor_class} for processing images).
• bool_masked_pos (torch.BoolTensor of shape(batch_size, num_patches)) —Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
• head_mask (torch.Tensor of shape(num_heads,) or(num_layers, num_heads),optional) —Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:
  • 1 indicates the head isnot masked,
  • 0 indicates the head ismasked.
• output_attentions (bool,optional) —Whether or not to return the attentions tensors of all attention layers. Seeattentions under returnedtensors for more detail.
• output_hidden_states (bool,optional) —Whether or not to return the hidden states of all layers. Seehidden_states under returned tensors formore detail.
• interpolate_pos_encoding (bool,optional) —Whether to interpolate the pre-trained position encodings.
• return_dict (bool,optional) —Whether or not to return aModelOutput instead of a plain tuple.

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

• loss (torch.FloatTensor of shape(1,),optional, returned whenbool_masked_pos is provided) — Reconstruction loss.
• reconstruction (torch.FloatTensor of shape(batch_size, num_channels, height, width)) — Reconstructed / completed images.
• 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 stage) of shape(batch_size, sequence_length, hidden_size). Hidden-states(also called feature maps) of the model at the output of each stage.
• attentions (tuple(torch.FloatTensor),optional, returned whenoutput_attentions=True is passed or when
• config.output_attentions=True):Tuple oftorch.FloatTensor (one for each layer) of shape(batch_size, num_heads, patch_size, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average inthe self-attention heads.

>>>from transformersimport AutoImageProcessor, ViTForMaskedImageModeling>>>import torch>>>from PILimport Image>>>import requests>>>url ="http://images.cocodataset.org/val2017/000000039769.jpg">>>image = Image.open(requests.get(url, stream=True).raw)>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")>>>model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k")>>>num_patches = (model.config.image_size // model.config.patch_size) **2>>>pixel_values = image_processor(images=image, return_tensors="pt").pixel_values>>># create random boolean mask of shape (batch_size, num_patches)>>>bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()>>>outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)>>>loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction>>>list(reconstructed_pixel_values.shape)[1,3,224,224]

Parameters

• pixel_values (torch.Tensor of shape(batch_size, num_channels, image_size, image_size),optional) —The tensors corresponding to the input images. Pixel values can be obtained using{image_processor_class}. See{image_processor_class}.__call__ for details ({processor_class} uses{image_processor_class} for processing images).
• head_mask (torch.Tensor of shape(num_heads,) or(num_layers, num_heads),optional) —Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:
  • 1 indicates the head isnot masked,
  • 0 indicates the head ismasked.
• labels (torch.LongTensor of shape(batch_size,),optional) —Labels for computing the image classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1 a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).
• output_attentions (bool,optional) —Whether or not to return the attentions tensors of all attention layers. Seeattentions under returnedtensors for more detail.
• output_hidden_states (bool,optional) —Whether or not to return the hidden states of all layers. Seehidden_states under returned tensors formore detail.
• interpolate_pos_encoding (bool,optional) —Whether to interpolate the pre-trained position encodings.
• return_dict (bool,optional) —Whether or not to return aModelOutput instead of a plain tuple.

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

• loss (torch.FloatTensor of shape(1,),optional, returned whenlabels is provided) — Classification (or regression if config.num_labels==1) loss.

• logits (torch.FloatTensor of shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

• 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 stage) of shape(batch_size, sequence_length, hidden_size). Hidden-states(also called feature maps) of the model at the output of each stage.

• 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, patch_size, sequence_length).

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

>>>from transformersimport AutoImageProcessor, ViTForImageClassification>>>import torch>>>from datasetsimport load_dataset>>>dataset = load_dataset("huggingface/cats-image")>>>image = dataset["test"]["image"][0]>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")>>>model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")>>>inputs = image_processor(image, return_tensors="pt")>>>with torch.no_grad():...    logits = model(**inputs).logits>>># model predicts one of the 1000 ImageNet classes>>>predicted_label = logits.argmax(-1).item()>>>print(model.config.id2label[predicted_label])...

Parameters

• pixel_values (np.ndarray,tf.Tensor,list[tf.Tensor] `dict[str, tf.Tensor] ordict[str, np.ndarray] and each example must have the shape(batch_size, num_channels, height, width)) —Pixel values. Pixel values can be obtained usingAutoImageProcessor. SeeViTImageProcessor.call()for details.
• head_mask (np.ndarray ortf.Tensor of shape(num_heads,) or(num_layers, num_heads),optional) —Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:
  • 1 indicates the head isnot masked,
  • 0 indicates the head ismasked.
• output_attentions (bool,optional) —Whether or not to return the attentions tensors of all attention layers. Seeattentions under returnedtensors for more detail. This argument can be used only in eager mode, in graph mode the value in theconfig will be used instead.
• output_hidden_states (bool,optional) —Whether or not to return the hidden states of all layers. Seehidden_states under returned tensors formore detail. This argument can be used only in eager mode, in graph mode the value in the config will beused instead.
• interpolate_pos_encoding (bool,optional) —Whether to interpolate the pre-trained position encodings.
• return_dict (bool,optional) —Whether or not to return aModelOutput instead of a plain tuple. This argument can be used ineager mode, in graph mode the value will always be set to True.
• training (bool,optional, defaults to `False“) —Whether or not to use the model in training mode (some modules like dropout modules have differentbehaviors between training and evaluation).

Atransformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or a tuple oftf.Tensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising various elements depending on theconfiguration (ViTConfig) and inputs.

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

• pooler_output (tf.Tensor of shape(batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by aLinear layer and a Tanh activation function. The Linear layer weights are trained from the next sentenceprediction (classification) objective during pretraining.

  This output is usuallynot a good summary of the semantic content of the input, you’re often better withaveraging or pooling the sequence of hidden-states for the whole input sequence.

• hidden_states (tuple(tf.Tensor),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor (one for the output of the embeddings + 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 initial embedding outputs.

• attentions (tuple(tf.Tensor),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor (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 AutoImageProcessor, TFViTModel>>>from datasetsimport load_dataset>>>dataset = load_dataset("huggingface/cats-image")>>>image = dataset["test"]["image"][0]>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")>>>model = TFViTModel.from_pretrained("google/vit-base-patch16-224-in21k")>>>inputs = image_processor(image, return_tensors="tf")>>>outputs = model(**inputs)>>>last_hidden_states = outputs.last_hidden_state>>>list(last_hidden_states.shape)[1,197,768]

Parameters

• pixel_values (np.ndarray,tf.Tensor,list[tf.Tensor] `dict[str, tf.Tensor] ordict[str, np.ndarray] and each example must have the shape(batch_size, num_channels, height, width)) —Pixel values. Pixel values can be obtained usingAutoImageProcessor. SeeViTImageProcessor.call()for details.
• head_mask (np.ndarray ortf.Tensor of shape(num_heads,) or(num_layers, num_heads),optional) —Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:
  • 1 indicates the head isnot masked,
  • 0 indicates the head ismasked.
• output_attentions (bool,optional) —Whether or not to return the attentions tensors of all attention layers. Seeattentions under returnedtensors for more detail. This argument can be used only in eager mode, in graph mode the value in theconfig will be used instead.
• output_hidden_states (bool,optional) —Whether or not to return the hidden states of all layers. Seehidden_states under returned tensors formore detail. This argument can be used only in eager mode, in graph mode the value in the config will beused instead.
• interpolate_pos_encoding (bool,optional) —Whether to interpolate the pre-trained position encodings.
• return_dict (bool,optional) —Whether or not to return aModelOutput instead of a plain tuple. This argument can be used ineager mode, in graph mode the value will always be set to True.
• training (bool,optional, defaults to `False“) —Whether or not to use the model in training mode (some modules like dropout modules have differentbehaviors between training and evaluation).
• labels (tf.Tensor ornp.ndarray of shape(batch_size,),optional) —Labels for computing the image classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1 a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).

Atransformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple oftf.Tensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising various elements depending on theconfiguration (ViTConfig) and inputs.

• loss (tf.Tensor of shape(batch_size, ),optional, returned whenlabels is provided) — Classification (or regression if config.num_labels==1) loss.

• logits (tf.Tensor of shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

• hidden_states (tuple(tf.Tensor),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor (one for the output of the embeddings + 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 initial embedding outputs.

• attentions (tuple(tf.Tensor),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor (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 AutoImageProcessor, TFViTForImageClassification>>>import tensorflowas tf>>>from datasetsimport load_dataset>>>dataset = load_dataset("huggingface/cats-image"))>>>image = dataset["test"]["image"][0]>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")>>>model = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224")>>>inputs = image_processor(image, return_tensors="tf")>>>logits = model(**inputs).logits>>># model predicts one of the 1000 ImageNet classes>>>predicted_label =int(tf.math.argmax(logits, axis=-1))>>>print(model.config.id2label[predicted_label])Egyptian cat

Atransformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (<class 'transformers.models.vit.configuration_vit.ViTConfig'>) and inputs.

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

• pooler_output (jnp.ndarray of shape(batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by aLinear layer and a Tanh activation function. The Linear layer weights are trained from the next sentenceprediction (classification) objective during pretraining.

• hidden_states (tuple(jnp.ndarray),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray (one for the output of the embeddings + 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 initial embedding outputs.

• attentions (tuple(jnp.ndarray),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray (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 AutoImageProcessor, FlaxViTModel>>>from PILimport Image>>>import requests>>>url ="http://images.cocodataset.org/val2017/000000039769.jpg">>>image = Image.open(requests.get(url, stream=True).raw)>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")>>>model = FlaxViTModel.from_pretrained("google/vit-base-patch16-224-in21k")>>>inputs = image_processor(images=image, return_tensors="np")>>>outputs = model(**inputs)>>>last_hidden_states = outputs.last_hidden_state

Atransformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (<class 'transformers.models.vit.configuration_vit.ViTConfig'>) and inputs.

• logits (jnp.ndarray of shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

• hidden_states (tuple(jnp.ndarray),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray (one for the output of the embeddings + 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 initial embedding outputs.

• attentions (tuple(jnp.ndarray),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray (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 AutoImageProcessor, FlaxViTForImageClassification>>>from PILimport Image>>>import jax>>>import requests>>>url ="http://images.cocodataset.org/val2017/000000039769.jpg">>>image = Image.open(requests.get(url, stream=True).raw)>>>image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")>>>model = FlaxViTForImageClassification.from_pretrained("google/vit-base-patch16-224")>>>inputs = image_processor(images=image, return_tensors="np")>>>outputs = model(**inputs)>>>logits = outputs.logits>>># model predicts one of the 1000 ImageNet classes>>>predicted_class_idx = jax.numpy.argmax(logits, axis=-1)>>>print("Predicted class:", model.config.id2label[predicted_class_idx.item()])