



import copy

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


__UpperCAmelCase	       =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__							):
       UpperCAmelCase__    : int       					=  "rag"
       UpperCAmelCase__    : List[Any]       					=  True
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=" / ",      SCREAMING_SNAKE_CASE_=" // ",      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=300,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=8,      SCREAMING_SNAKE_CASE_="wiki_dpr",      SCREAMING_SNAKE_CASE_="train",      SCREAMING_SNAKE_CASE_="compressed",      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          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   :  Union[str, Any]						     =     kwargs.pop('question_encoder'	)
          UpperCamelCase   :  str						     =     question_encoder_config.pop('model_type'	)
          UpperCamelCase   :  Optional[Any]						     =     kwargs.pop('generator'	)
          UpperCamelCase   :  List[Any]						     =     decoder_config.pop('model_type'	)

          from ..auto.configuration_auto import AutoConfig

          UpperCamelCase   :  str						     =     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     reduce_loss
          UpperCamelCase   :  Optional[Any]						     =     label_smoothing
          UpperCamelCase   :  int						     =     exclude_bos_score
          UpperCamelCase   :  Tuple						     =     do_marginalize

          UpperCamelCase   :  Optional[Any]						     =     title_sep
          UpperCamelCase   :  Any						     =     doc_sep
          UpperCamelCase   :  List[str]						     =     n_docs
          UpperCamelCase   :  Optional[int]						     =     max_combined_length

          UpperCamelCase   :  List[Any]						     =     dataset
          UpperCamelCase   :  Union[str, Any]						     =     dataset_split
          UpperCamelCase   :  str						     =     index_name

          UpperCamelCase   :  List[str]						     =     retrieval_vector_size
          UpperCamelCase   :  Any						     =     retrieval_batch_size
          UpperCamelCase   :  Optional[Any]						     =     passages_path
          UpperCamelCase   :  Dict						     =     index_path
          UpperCamelCase   :  Optional[Any]						     =     use_dummy_dataset

          UpperCamelCase   :  Any						     =     output_retrieved

          UpperCamelCase   :  Optional[int]						     =     do_deduplication

          UpperCamelCase   :  List[str]						     =     use_cache

          if self.forced_eos_token_id is None:
             UpperCamelCase   :  int						     =     getattr(self.generator,      'forced_eos_token_id',      SCREAMING_SNAKE_CASE_	)
       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		PretrainedConfig:
          return cls(question_encoder=question_encoder_config.to_dict(),      generator=generator_config.to_dict(),      **SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  str						     =     copy.deepcopy(self.__dict__	)
          UpperCamelCase   :  str						     =     self.question_encoder.to_dict()
          UpperCamelCase   :  Union[str, Any]						     =     self.generator.to_dict()
          UpperCamelCase   :  Optional[Any]						     =     self.__class__.model_type
          return output








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



