% &h4dZddlmZddlmZmZmZmZddlZddlm Z ddl m Z ddl m Z dd lmZdd lmZmZmZmZd d lmZd ZdZedeGddeZeGddeZdZedGddeZgdZy)zPyTorch ColPali model) dataclass)ListOptionalTupleUnionN)nn)AutoModelForImageTextToText)Cache)PreTrainedModel) ModelOutputadd_start_docstrings%add_start_docstrings_to_model_forwardreplace_return_docstrings) ColPaliConfigraL This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ColPaliConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. zUThe bare ColPali model outputting raw hidden-states without any specific head on top.ceZdZeZdZgZdZy)ColPaliPreTrainedModelmodelct|jdr|jjn)|jjjj}t |t jt jfrY|jjjd||j%|jjjyyt |t jrf|jjjd||j2|jj|jjyyy)Ninitializer_rangeg)meanstd)hasattrconfigr vlm_config text_config isinstancerLinearConv2dweightdatanormal_biaszero_ Embedding padding_idx)selfmodulers /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/colpali/modeling_colpali.py _init_weightsz$ColPaliPreTrainedModel._init_weights@st{{$78 KK ) )''33EE fryy"))4 5 MM   & &CS & 9{{&   &&('  - MM   & &CS & 9!!- ""6#5#56<<>..N)__name__ __module__ __qualname__r config_classbase_model_prefix_no_split_modulesr+r,r*rr7s !L?r,rceZdZUdZdZeejed<dZ eejed<dZ ee e ejefed<dZeeejed<dZeeejed<dZeejed<y) ColPaliForRetrievalOutputa/ Base class for ColPali embeddings output. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The embeddings of the model. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.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 when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.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-attention heads. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder after projecting last hidden state. Nloss embeddingspast_key_values hidden_states attentionsimage_hidden_states)r-r.r/__doc__r6rtorch FloatTensor__annotations__r7Tensorr8rrr r9rr:r;r3r,r*r5r5Qs<)-D(5$$ %,)-J&-GKOXeD):):$;U$BCDK8