Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LLaDADecoderLayer`] Args: config: LLaDAConfig

Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`SiglipEncoderLayer`]. Args:

LLaDARotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla

LLaDARotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev

Resize the input image so that its longest side and shortest side are within a specified range, ensuring that both sides are divisible by a specif

Multihead Attention Pooling.

Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for ima

r""" This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a Siglip text encoder acco

r""" This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a Siglip vision encoder

Embeds scalar timesteps into vector representations.

Multi-headed attention from 'Attention Is All You Need' paper

r""" This is the configuration class to store the configuration of a [`LLaDAModel`]. It is used to instantiate an LLaDA model according to the

r""" This is the configuration class to store the configuration of a [`LLaDAModel`]. It is used to instantiate an LLaDA model according to the

LLaDA flash attention module. This module inherits from `LLaDAAttention` as the weights of the module stays untouched. The only required chan

LLaDA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `LLaDAAttention` as the weights of t

Multi-headed attention from 'Attention Is All You Need' paper

r""" [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to instantiate a Siglip model acc

SiglipAttention flash attention module. This module inherits from `SiglipAttention` as the weights of the module stays untouched. The only re

r""" Constructs a SigLIP image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

r""" Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor. [`SiglipProcessor`] o

Siglip attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `SiglipAttention` as the weights of

Base class for text model's outputs that also contains a pooling of the last hidden states. Args: text_embeds (`torch.FloatTensor` o

Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTo

r""" This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a Siglip vision encoder

Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. Args: image_embe