




'''simple docstring'''






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(lowerCAmelCase_							)
class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"rag"
     A_											=			True





     def __init__(     self     :					Any     ,							__lowerCamelCase     :					Optional[int]=None     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					str=None     ,							__lowerCamelCase     :					Union[str, Any]=None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					Optional[int]=" / "     ,							__lowerCamelCase     :					List[Any]=" // "     ,							__lowerCamelCase     :					Optional[int]=5     ,							__lowerCamelCase     :					Tuple=300     ,							__lowerCamelCase     :					Optional[int]=768     ,							__lowerCamelCase     :					Optional[Any]=8     ,							__lowerCamelCase     :					Any="wiki_dpr"     ,							__lowerCamelCase     :					Any="train"     ,							__lowerCamelCase     :					Dict="compressed"     ,							__lowerCamelCase     :					int=None     ,							__lowerCamelCase     :					Optional[int]=None     ,							__lowerCamelCase     :					List[str]=False     ,							__lowerCamelCase     :					int=False     ,							__lowerCamelCase     :					Tuple=0.0     ,							__lowerCamelCase     :					Union[str, Any]=True     ,							__lowerCamelCase     :					int=False     ,							__lowerCamelCase     :					Any=False     ,							__lowerCamelCase     :					List[str]=False     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					Optional[Any]=None     ,							**__lowerCamelCase     :					List[str]     ,							):
           super().__init__(
               bos_token_id=__lowerCamelCase     ,							pad_token_id=__lowerCamelCase     ,							eos_token_id=__lowerCamelCase     ,							decoder_start_token_id=__lowerCamelCase     ,							forced_eos_token_id=__lowerCamelCase     ,							is_encoder_decoder=__lowerCamelCase     ,							prefix=__lowerCamelCase     ,							vocab_size=__lowerCamelCase     ,							**__lowerCamelCase     ,							)
           assert (
               "question_encoder" in kwargs and "generator" in kwargs
           ), "Config has to be initialized with question_encoder and generator config"
           snake_case__       :   List[Any]	      =    kwargs.pop('question_encoder'			)
           snake_case__       :   Any	      =    question_encoder_config.pop('model_type'			)
           snake_case__       :   int	      =    kwargs.pop('generator'			)
           snake_case__       :   Optional[Any]	      =    decoder_config.pop('model_type'			)

           from ..auto.configuration_auto import AutoConfig

           snake_case__       :   Optional[int]	      =    AutoConfig.for_model(__lowerCamelCase     ,							**__lowerCamelCase			)
           snake_case__       :   Dict	      =    AutoConfig.for_model(__lowerCamelCase     ,							**__lowerCamelCase			)

           snake_case__       :   Any	      =    reduce_loss
           snake_case__       :   Any	      =    label_smoothing
           snake_case__       :   Union[str, Any]	      =    exclude_bos_score
           snake_case__       :   List[str]	      =    do_marginalize

           snake_case__       :   Dict	      =    title_sep
           snake_case__       :   Dict	      =    doc_sep
           snake_case__       :   Dict	      =    n_docs
           snake_case__       :   Tuple	      =    max_combined_length

           snake_case__       :   Dict	      =    dataset
           snake_case__       :   Dict	      =    dataset_split
           snake_case__       :   Optional[int]	      =    index_name

           snake_case__       :   Tuple	      =    retrieval_vector_size
           snake_case__       :   Any	      =    retrieval_batch_size
           snake_case__       :   Tuple	      =    passages_path
           snake_case__       :   Optional[Any]	      =    index_path
           snake_case__       :   Tuple	      =    use_dummy_dataset

           snake_case__       :   Optional[int]	      =    output_retrieved

           snake_case__       :   List[Any]	      =    do_deduplication

           snake_case__       :   Optional[Any]	      =    use_cache

           if self.forced_eos_token_id is None:
                 snake_case__       :   str	      =    getattr(self.generator     ,							'forced_eos_token_id'     ,							__lowerCamelCase			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[Any]     ,							__lowerCamelCase     :					PretrainedConfig     ,							__lowerCamelCase     :					PretrainedConfig     ,							**__lowerCamelCase     :					Optional[Any]			):
           return cls(question_encoder=question_encoder_config.to_dict()     ,							generator=generator_config.to_dict()     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Any	      =    copy.deepcopy(self.__dict__			)
           snake_case__       :   str	      =    self.question_encoder.to_dict()
           snake_case__       :   Union[str, Any]	      =    self.generator.to_dict()
           snake_case__       :   int	      =    self.__class__.model_type
           return output






'''simple docstring'''






import functools






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Any	      =    len(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   List[str]	      =    len(__SCREAMING_SNAKE_CASE		)

      @functools.cache
      def min_distance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		) -> int:
            # if first word index is overflow - delete all from the second word
            if indexa >= len_worda:
                  return len_worda - indexa
            # if second word index is overflow - delete all from the first word
            if indexa >= len_worda:
                  return len_worda - indexa
            snake_case__       :   Union[str, Any]	      =    int(worda[indexa] != worda[indexa]		)  # current letters not identical
            return min(
                1 + min_distance(indexa + 1				,      __SCREAMING_SNAKE_CASE		)				,      1 + min_distance(__SCREAMING_SNAKE_CASE				,      indexa + 1		)				,      diff + min_distance(indexa + 1				,      indexa + 1		)				,      )

      return min_distance(0				,      0		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
