






import copy

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


lowerCamelCase     :							int    =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       __a  (   a__	):
 _lowerCAmelCase	:	str				=			'''rag'''
 _lowerCAmelCase	:	Optional[int]				=			True







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


      '''simple docstring'''






      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__      :       Dict	    =  kwargs.pop("question_encoder"						)
      UpperCamelCase__      :       str	    =  question_encoder_config.pop("model_type"						)
      UpperCamelCase__      :       List[Any]	    =  kwargs.pop("generator"						)
      UpperCamelCase__      :       Tuple	    =  decoder_config.pop("model_type"						)

      from ..auto.configuration_auto import AutoConfig

      UpperCamelCase__      :       Optional[int]	    =  AutoConfig.for_model(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  AutoConfig.for_model(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  reduce_loss
      UpperCamelCase__      :       Optional[Any]	    =  label_smoothing
      UpperCamelCase__      :       List[Any]	    =  exclude_bos_score
      UpperCamelCase__      :       List[str]	    =  do_marginalize

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

      UpperCamelCase__      :       int	    =  dataset
      UpperCamelCase__      :       Any	    =  dataset_split
      UpperCamelCase__      :       str	    =  index_name

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

      UpperCamelCase__      :       int	    =  output_retrieved

      UpperCamelCase__      :       List[str]	    =  do_deduplication

      UpperCamelCase__      :       Tuple	    =  use_cache

      if self.forced_eos_token_id is None:
           UpperCamelCase__      :       Tuple	    =  getattr(self.generator							,       "forced_eos_token_id"							,       SCREAMING_SNAKE_CASE						)







 @classmethod
 def  __lowercase   (		cls :       str							,       SCREAMING_SNAKE_CASE :       PretrainedConfig							,       SCREAMING_SNAKE_CASE :       PretrainedConfig							,       **SCREAMING_SNAKE_CASE :       List[Any]						):


      '''simple docstring'''






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







 def  __lowercase   (		self :       List[Any]						):


      '''simple docstring'''






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







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[int]:
     UpperCamelCase__      :       Optional[Any]	    =  0
     UpperCamelCase__      :       Any	    =  len(__lowerCAmelCase      ) - 1

     while i < j:
          if nums[i] + nums[j] == target:
               return [i, j]
          elif nums[i] + nums[j] < target:
               UpperCamelCase__      :       Optional[int]	    =  i + 1
          else:
               UpperCamelCase__      :       Dict	    =  j - 1

     return []


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")