


import copy

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


__UpperCamelCase				:				Optional[int]  				= 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(_UpperCAmelCase)
class 					__magic_name__		(   _UpperCAmelCase):
     A:						List[str]								=					"rag"
     A:						Tuple								=					True


     def __init__(					self							:   str							,		lowerCamelCase__							:   Any=None							,		lowerCamelCase__							:   List[Any]=True							,		lowerCamelCase__							:   str=None							,		lowerCamelCase__							:   int=None							,		lowerCamelCase__							:   Tuple=None							,		lowerCamelCase__							:   Tuple=None							,		lowerCamelCase__							:   Optional[Any]=None							,		lowerCamelCase__							:   Any=" / "							,		lowerCamelCase__							:   Tuple=" // "							,		lowerCamelCase__							:   List[Any]=5							,		lowerCamelCase__							:   List[str]=300							,		lowerCamelCase__							:   List[Any]=768							,		lowerCamelCase__							:   Dict=8							,		lowerCamelCase__							:   Any="wiki_dpr"							,		lowerCamelCase__							:   List[Any]="train"							,		lowerCamelCase__							:   Optional[Any]="compressed"							,		lowerCamelCase__							:   Any=None							,		lowerCamelCase__							:   Union[str, Any]=None							,		lowerCamelCase__							:   Any=False							,		lowerCamelCase__							:   Optional[Any]=False							,		lowerCamelCase__							:   List[Any]=0.0							,		lowerCamelCase__							:   Optional[int]=True							,		lowerCamelCase__							:   Optional[Any]=False							,		lowerCamelCase__							:   Dict=False							,		lowerCamelCase__							:   Union[str, Any]=False							,		lowerCamelCase__							:   List[str]=True							,		lowerCamelCase__							:   Optional[Any]=None							,		**lowerCamelCase__							:   int							,		)     ->					str:





      '''simple docstring'''






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

      from ..auto.configuration_auto import AutoConfig

      UpperCamelCase__    :			Optional[int]      				=      AutoConfig.for_model(lowercase_							,		**lowercase_	)
      UpperCamelCase__    :			Any      				=      AutoConfig.for_model(lowercase_							,		**lowercase_	)

      UpperCamelCase__    :			Dict      				=      reduce_loss
      UpperCamelCase__    :			Union[str, Any]      				=      label_smoothing
      UpperCamelCase__    :			int      				=      exclude_bos_score
      UpperCamelCase__    :			List[Any]      				=      do_marginalize

      UpperCamelCase__    :			int      				=      title_sep
      UpperCamelCase__    :			Any      				=      doc_sep
      UpperCamelCase__    :			Dict      				=      n_docs
      UpperCamelCase__    :			Optional[int]      				=      max_combined_length

      UpperCamelCase__    :			List[str]      				=      dataset
      UpperCamelCase__    :			Optional[Any]      				=      dataset_split
      UpperCamelCase__    :			Dict      				=      index_name

      UpperCamelCase__    :			List[Any]      				=      retrieval_vector_size
      UpperCamelCase__    :			str      				=      retrieval_batch_size
      UpperCamelCase__    :			Any      				=      passages_path
      UpperCamelCase__    :			Union[str, Any]      				=      index_path
      UpperCamelCase__    :			str      				=      use_dummy_dataset

      UpperCamelCase__    :			List[Any]      				=      output_retrieved

      UpperCamelCase__    :			int      				=      do_deduplication

      UpperCamelCase__    :			List[str]      				=      use_cache

      if self.forced_eos_token_id is None:
       UpperCamelCase__    :			Optional[Any]      				=      getattr(self.generator							,		'''forced_eos_token_id'''							,		lowercase_	)


     @classmethod
     def    UpperCAmelCase__		(					cls							:   Tuple							,		lowerCamelCase__							:   PretrainedConfig							,		lowerCamelCase__							:   PretrainedConfig							,		**lowerCamelCase__							:   int	)     ->					Optional[int]:





      '''simple docstring'''






      return cls(question_encoder=question_encoder_config.to_dict()							,		generator=generator_config.to_dict()							,		**lowercase_	)


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					Optional[int]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      copy.deepcopy(self.__dict__	)
      UpperCamelCase__    :			List[Any]      				=      self.question_encoder.to_dict()
      UpperCamelCase__    :			Optional[int]      				=      self.generator.to_dict()
      UpperCamelCase__    :			str      				=      self.__class__.model_type
      return output



'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      bool:
							return numa ^ numa < 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()





