g'!ddZddlmZddlmZdZeeGddeZdS)zRAG model configuration)PretrainedConfig)add_start_docstringsa) [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: title_sep (`str`, *optional*, defaults to `" / "`): Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`]. doc_sep (`str`, *optional*, defaults to `" // "`): Separator inserted between the text of the retrieved document and the original input when calling [`RagRetriever`]. n_docs (`int`, *optional*, defaults to 5): Number of documents to retrieve. max_combined_length (`int`, *optional*, defaults to 300): Max length of contextualized input returned by [`~RagRetriever.__call__`]. retrieval_vector_size (`int`, *optional*, defaults to 768): Dimensionality of the document embeddings indexed by [`RagRetriever`]. retrieval_batch_size (`int`, *optional*, defaults to 8): Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated [`RagRetriever`]. dataset (`str`, *optional*, defaults to `"wiki_dpr"`): A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids using `datasets.list_datasets()`). dataset_split (`str`, *optional*, defaults to `"train"`) Which split of the `dataset` to load. index_name (`str`, *optional*, defaults to `"compressed"`) The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and `"compressed"`. index_path (`str`, *optional*) The path to the serialized faiss index on disk. passages_path (`str`, *optional*): A path to text passages compatible with the faiss index. Required if using [`~models.rag.retrieval_rag.LegacyIndex`] use_dummy_dataset (`bool`, *optional*, defaults to `False`) Whether to load a "dummy" variant of the dataset specified by `dataset`. label_smoothing (`float`, *optional*, defaults to 0.0): Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. do_marginalize (`bool`, *optional*, defaults to `False`): If `True`, the logits are marginalized over all documents by making use of `torch.nn.functional.log_softmax`. reduce_loss (`bool`, *optional*, defaults to `False`): Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation. do_deduplication (`bool`, *optional*, defaults to `True`): Whether or not to deduplicate the generations from different context documents for a given input. Has to be set to `False` if used while training with distributed backend. exclude_bos_score (`bool`, *optional*, defaults to `False`): Whether or not to disregard the BOS token when computing the loss. output_retrieved(`bool`, *optional*, defaults to `False`): If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask` are returned. See returned tensors for more detail. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (`int`, *optional*): The id of the token to force as the last generated token when `max_length` is reached. 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