'''simple docstring'''




import copy

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


_a   :	str 							=   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(snake_case__   )
class 		__A       (snake_case__   ):
       snake_case       :Union[str, Any]				    =					"rag"
       snake_case       :Any				    =					True






       def __init__(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=True				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=" / "				,     UpperCamelCase_=" // "				,     UpperCamelCase_=5				,     UpperCamelCase_=3_00				,     UpperCamelCase_=7_68				,     UpperCamelCase_=8				,     UpperCamelCase_="wiki_dpr"				,     UpperCamelCase_="train"				,     UpperCamelCase_="compressed"				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=False				,     UpperCamelCase_=False				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_=False				,     UpperCamelCase_=False				,     UpperCamelCase_=False				,     UpperCamelCase_=True				,     UpperCamelCase_=None				,     **UpperCamelCase_				,     ):
              super().__init__(
                  bos_token_id=_SCREAMING_SNAKE_CASE				,     pad_token_id=_SCREAMING_SNAKE_CASE				,     eos_token_id=_SCREAMING_SNAKE_CASE				,     decoder_start_token_id=_SCREAMING_SNAKE_CASE				,     forced_eos_token_id=_SCREAMING_SNAKE_CASE				,     is_encoder_decoder=_SCREAMING_SNAKE_CASE				,     prefix=_SCREAMING_SNAKE_CASE				,     vocab_size=_SCREAMING_SNAKE_CASE				,     **_SCREAMING_SNAKE_CASE				,     )
              assert (
                  "question_encoder" in kwargs and "generator" in kwargs
              ), "Config has to be initialized with question_encoder and generator config"
              __UpperCAmelCase  :				int       				=		kwargs.pop("question_encoder"      )
              __UpperCAmelCase  :				List[str]       				=		question_encoder_config.pop("model_type"      )
              __UpperCAmelCase  :				List[Any]       				=		kwargs.pop("generator"      )
              __UpperCAmelCase  :				Union[str, Any]       				=		decoder_config.pop("model_type"      )

              from ..auto.configuration_auto import AutoConfig

              __UpperCAmelCase  :				int       				=		AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,     **_SCREAMING_SNAKE_CASE      )
              __UpperCAmelCase  :				Dict       				=		AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,     **_SCREAMING_SNAKE_CASE      )

              __UpperCAmelCase  :				Any       				=		reduce_loss
              __UpperCAmelCase  :				str       				=		label_smoothing
              __UpperCAmelCase  :				Tuple       				=		exclude_bos_score
              __UpperCAmelCase  :				Optional[Any]       				=		do_marginalize

              __UpperCAmelCase  :				Union[str, Any]       				=		title_sep
              __UpperCAmelCase  :				Any       				=		doc_sep
              __UpperCAmelCase  :				Optional[int]       				=		n_docs
              __UpperCAmelCase  :				int       				=		max_combined_length

              __UpperCAmelCase  :				Optional[int]       				=		dataset
              __UpperCAmelCase  :				Any       				=		dataset_split
              __UpperCAmelCase  :				Tuple       				=		index_name

              __UpperCAmelCase  :				Optional[Any]       				=		retrieval_vector_size
              __UpperCAmelCase  :				List[str]       				=		retrieval_batch_size
              __UpperCAmelCase  :				Tuple       				=		passages_path
              __UpperCAmelCase  :				List[str]       				=		index_path
              __UpperCAmelCase  :				Tuple       				=		use_dummy_dataset

              __UpperCAmelCase  :				Optional[Any]       				=		output_retrieved

              __UpperCAmelCase  :				Dict       				=		do_deduplication

              __UpperCAmelCase  :				str       				=		use_cache

              if self.forced_eos_token_id is None:
                     __UpperCAmelCase  :				List[Any]       				=		getattr(self.generator				,     "forced_eos_token_id"				,     _SCREAMING_SNAKE_CASE      )






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              return cls(question_encoder=question_encoder_config.to_dict()				,     generator=generator_config.to_dict()				,     **_SCREAMING_SNAKE_CASE      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		copy.deepcopy(self.__dict__      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.question_encoder.to_dict()
              __UpperCAmelCase  :				Union[str, Any]       				=		self.generator.to_dict()
              __UpperCAmelCase  :				str       				=		self.__class__.model_type
              return output







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number | (1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number & ~(1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number ^ (1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return ((number >> position) & 1) == 1

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return int((number & (1 << position)) != 0						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






