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

Convert embeddings back to logits over vocabulary. This is the inverse operation of embed_language_tokens(). Equivalent to JAX versio

使用真实 LeRobot 数据的示例

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

Returns config for specified gemma variant.

Returns config for specified gemma variant.

Returns the freeze filter based on the model config.

Returns the freeze filter based on the model config.

Get parameters from model, handling DDP wrapper.

Get state dict from model, handling DDP wrapper.