




"""simple docstring"""


import copy

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


_A													=      r"""
    [`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. Usually set to
            `eos_token_id`.
"""


@add_start_docstrings(a_						)
class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[int]     				=     "rag"
   _lowerCamelCase    :Union[str, Any]     				=     True






   def __init__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		Optional[Any]=True   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Optional[int]=None   ,     UpperCamelCase       :		Union[str, Any]=None   ,     UpperCamelCase       :		Union[str, Any]=" / "   ,     UpperCamelCase       :		Optional[int]=" // "   ,     UpperCamelCase       :		str=5   ,     UpperCamelCase       :		str=3_00   ,     UpperCamelCase       :		Any=7_68   ,     UpperCamelCase       :		Union[str, Any]=8   ,     UpperCamelCase       :		List[Any]="wiki_dpr"   ,     UpperCamelCase       :		Optional[int]="train"   ,     UpperCamelCase       :		int="compressed"   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		Optional[Any]=None   ,     UpperCamelCase       :		List[Any]=False   ,     UpperCamelCase       :		int=False   ,     UpperCamelCase       :		Any=0.0   ,     UpperCamelCase       :		Dict=True   ,     UpperCamelCase       :		List[str]=False   ,     UpperCamelCase       :		Any=False   ,     UpperCamelCase       :		Optional[int]=False   ,     UpperCamelCase       :		str=True   ,     UpperCamelCase       :		Any=None   ,     **UpperCamelCase       :		str   ,     )			->						List[Any]:

         """simple docstring"""
         super().__init__(
             bos_token_id=UpperCamelCase   ,     pad_token_id=UpperCamelCase   ,     eos_token_id=UpperCamelCase   ,     decoder_start_token_id=UpperCamelCase   ,     forced_eos_token_id=UpperCamelCase   ,     is_encoder_decoder=UpperCamelCase   ,     prefix=UpperCamelCase   ,     vocab_size=UpperCamelCase   ,     **UpperCamelCase   ,     )
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         lowerCAmelCase__      :			Any   =	kwargs.pop("""question_encoder"""   )
         lowerCAmelCase__      :			Any   =	question_encoder_config.pop("""model_type"""   )
         lowerCAmelCase__      :			Tuple   =	kwargs.pop("""generator"""   )
         lowerCAmelCase__      :			Optional[int]   =	decoder_config.pop("""model_type"""   )

         from ..auto.configuration_auto import AutoConfig

         lowerCAmelCase__      :			Union[str, Any]   =	AutoConfig.for_model(UpperCamelCase   ,     **UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	AutoConfig.for_model(UpperCamelCase   ,     **UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	reduce_loss
         lowerCAmelCase__      :			Any   =	label_smoothing
         lowerCAmelCase__      :			int   =	exclude_bos_score
         lowerCAmelCase__      :			Tuple   =	do_marginalize

         lowerCAmelCase__      :			List[str]   =	title_sep
         lowerCAmelCase__      :			Dict   =	doc_sep
         lowerCAmelCase__      :			Union[str, Any]   =	n_docs
         lowerCAmelCase__      :			str   =	max_combined_length

         lowerCAmelCase__      :			Any   =	dataset
         lowerCAmelCase__      :			List[Any]   =	dataset_split
         lowerCAmelCase__      :			List[str]   =	index_name

         lowerCAmelCase__      :			Tuple   =	retrieval_vector_size
         lowerCAmelCase__      :			Any   =	retrieval_batch_size
         lowerCAmelCase__      :			Optional[int]   =	passages_path
         lowerCAmelCase__      :			str   =	index_path
         lowerCAmelCase__      :			Tuple   =	use_dummy_dataset

         lowerCAmelCase__      :			Optional[Any]   =	output_retrieved

         lowerCAmelCase__      :			Dict   =	do_deduplication

         lowerCAmelCase__      :			Optional[int]   =	use_cache

         if self.forced_eos_token_id is None:
               lowerCAmelCase__      :			Optional[int]   =	getattr(self.generator   ,     """forced_eos_token_id"""   ,     UpperCamelCase   )






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Dict   ,     UpperCamelCase       :		PretrainedConfig   ,     UpperCamelCase       :		PretrainedConfig   ,     **UpperCamelCase       :		Dict   )			->						PretrainedConfig:

         """simple docstring"""
         return cls(question_encoder=question_encoder_config.to_dict()   ,     generator=generator_config.to_dict()   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	copy.deepcopy(self.__dict__   )
         lowerCAmelCase__      :			List[Any]   =	self.question_encoder.to_dict()
         lowerCAmelCase__      :			List[Any]   =	self.generator.to_dict()
         lowerCAmelCase__      :			str   =	self.__class__.model_type
         return output











"""simple docstring"""


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *





