





import copy

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


lowercase_          =		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(UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	'rag'
   _UpperCamelCase   :		Union[str, Any]			 =	True


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





     """simple docstring"""







     super().__init__(
         bos_token_id=a      ,					pad_token_id=a      ,					eos_token_id=a      ,					decoder_start_token_id=a      ,					forced_eos_token_id=a      ,					is_encoder_decoder=a      ,					prefix=a      ,					vocab_size=a      ,					**a      ,					)
     assert (
         "question_encoder" in kwargs and "generator" in kwargs
     ), "Config has to be initialized with question_encoder and generator config"
     lowercase__           =  kwargs.pop('question_encoder'	)
     lowercase__           =  question_encoder_config.pop('model_type'	)
     lowercase__           =  kwargs.pop('generator'	)
     lowercase__           =  decoder_config.pop('model_type'	)

     from ..auto.configuration_auto import AutoConfig

     lowercase__           =  AutoConfig.for_model(a      ,					**a	)
     lowercase__           =  AutoConfig.for_model(a      ,					**a	)

     lowercase__           =  reduce_loss
     lowercase__           =  label_smoothing
     lowercase__           =  exclude_bos_score
     lowercase__           =  do_marginalize

     lowercase__           =  title_sep
     lowercase__           =  doc_sep
     lowercase__           =  n_docs
     lowercase__           =  max_combined_length

     lowercase__           =  dataset
     lowercase__           =  dataset_split
     lowercase__           =  index_name

     lowercase__           =  retrieval_vector_size
     lowercase__           =  retrieval_batch_size
     lowercase__           =  passages_path
     lowercase__           =  index_path
     lowercase__           =  use_dummy_dataset

     lowercase__           =  output_retrieved

     lowercase__           =  do_deduplication

     lowercase__           =  use_cache

     if self.forced_eos_token_id is None:
       lowercase__           =  getattr(self.generator      ,					'forced_eos_token_id'      ,					a	)


   @classmethod
   def 	SCREAMING_SNAKE_CASE_							(   cls      :    List[str]      ,					a      :    PretrainedConfig      ,					a      :    PretrainedConfig      ,					**a      :    Union[str, Any]	)->       PretrainedConfig:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  copy.deepcopy(self.__dict__	)
     lowercase__           =  self.question_encoder.to_dict()
     lowercase__           =  self.generator.to_dict()
     lowercase__           =  self.__class__.model_type
     return output









def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:

  if exponent == 1:
    return base
  if exponent % 2 == 0:
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						exponent // 2		,						_SCREAMING_SNAKE_CASE		) % modulo_value
    return (x * x) % modulo_value
  else:
    return (base * _modexpt(_SCREAMING_SNAKE_CASE		,						exponent - 1		,						_SCREAMING_SNAKE_CASE		)) % modulo_value




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = 1777		,						_SCREAMING_SNAKE_CASE = 1855		,						_SCREAMING_SNAKE_CASE = 8		)    ->       int:
  lowercase__           =  base
  for _ in range(1		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						10**digits		)

  return result


if __name__ == "__main__":
      print(f'''{solution() = }''')


