


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


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






@add_start_docstrings(A_		)
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  "rag"
    UpperCAmelCase__						:		Tuple              =  True






    def __init__(      self ,       A_=None ,       A_=True ,       A_=None ,       A_=None ,       A_=None ,       A_=None ,       A_=None ,       A_=" / " ,       A_=" // " ,       A_=5 ,       A_=300 ,       A_=768 ,       A_=8 ,       A_="wiki_dpr" ,       A_="train" ,       A_="compressed" ,       A_=None ,       A_=None ,       A_=False ,       A_=False ,       A_=0.0 ,       A_=True ,       A_=False ,       A_=False ,       A_=False ,       A_=True ,       A_=None ,       **A_ ,       )  ->	Tuple:
         super().__init__(
             bos_token_id=A_ ,       pad_token_id=A_ ,       eos_token_id=A_ ,       decoder_start_token_id=A_ ,       forced_eos_token_id=A_ ,       is_encoder_decoder=A_ ,       prefix=A_ ,       vocab_size=A_ ,       **A_ ,       )
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         __UpperCamelCase   							=kwargs.pop('question_encoder'   )
         __UpperCamelCase   							=question_encoder_config.pop('model_type'   )
         __UpperCamelCase   							=kwargs.pop('generator'   )
         __UpperCamelCase   							=decoder_config.pop('model_type'   )

         from ..auto.configuration_auto import AutoConfig

         __UpperCamelCase   							=AutoConfig.for_model(A_ ,       **A_   )
         __UpperCamelCase   							=AutoConfig.for_model(A_ ,       **A_   )

         __UpperCamelCase   							=reduce_loss
         __UpperCamelCase   							=label_smoothing
         __UpperCamelCase   							=exclude_bos_score
         __UpperCamelCase   							=do_marginalize

         __UpperCamelCase   							=title_sep
         __UpperCamelCase   							=doc_sep
         __UpperCamelCase   							=n_docs
         __UpperCamelCase   							=max_combined_length

         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=dataset_split
         __UpperCamelCase   							=index_name

         __UpperCamelCase   							=retrieval_vector_size
         __UpperCamelCase   							=retrieval_batch_size
         __UpperCamelCase   							=passages_path
         __UpperCamelCase   							=index_path
         __UpperCamelCase   							=use_dummy_dataset

         __UpperCamelCase   							=output_retrieved

         __UpperCamelCase   							=do_deduplication

         __UpperCamelCase   							=use_cache

         if self.forced_eos_token_id is None:
              __UpperCamelCase   							=getattr(self.generator ,       'forced_eos_token_id' ,       A_   )






    @classmethod
    def _a (      cls ,       A_ ,       A_ ,       **A_   )  ->	PretrainedConfig:
         return cls(question_encoder=question_encoder_config.to_dict() ,       generator=generator_config.to_dict() ,       **A_   )






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=copy.deepcopy(self.__dict__   )
         __UpperCamelCase   							=self.question_encoder.to_dict()
         __UpperCamelCase   							=self.generator.to_dict()
         __UpperCamelCase   							=self.__class__.model_type
         return output



from ....utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=None ,       A_=2048   )  ->	Any:
         __UpperCamelCase   							=config.__dict__
         __UpperCamelCase   							=modal_hidden_size
         if num_labels:
              __UpperCamelCase   							=num_labels





