Initialize the weights

Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_l

Tell JAX inside the worker process not to preallocate the GPU memory.

Converts a PyTree of arrays into a human-readable string for logging.

Get the assets directory for this config.

Checks that two PyTrees have the same structure and optionally checks shapes and dtypes. Creates a much nicer error message than if `jax.tree.map`

Get the checkpoint directory for this config.

Splits episode into action chunks.

Compose a sequence of transforms into a single transform.

Converts an image to uint8 if it is a float image. This is important for reducing the size of the image when sending it over the network.

Create a data config.

Create a new model, initializing parameters.

Create a data loader for training. Args: config: The training configuration. sharding: The sharding to use for the data loader (J

Create a policy from a trained checkpoint. Args: train_config: The training config to use to create the model. checkpoint_dir: Th

adarms_cond (`torch.Tensor` of shape `(batch_size, cond_dim)`, *optional*): Condition for ADARMS.

r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language mo

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression

r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language mo

r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language mo

Input shape: Batch x Time x Channel

Args: hidden_states (`torch.FloatTensor`): Input to the layer of shape `(batch, seq_len, embed_dim)`.

r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally,

r""" Examples: ```python >>> from transformers import AutoTokenizer, SiglipTextModel >>> model = SiglipTextModel.fro

r""" Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProce

r""" return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. Examples: ```python

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression lo

Apply FSDP sharding to a pytree of arrays based on the mesh shape. Args: pytree: A pytree to be apply sharding specified by the mesh, not

Get a config by name.

Returns config for specified gemma variant.