

'''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(_a						)
class 							_snake_case (       _a						):
   _A       :     Optional[int]       =					'''rag'''
   _A       :     Any       =					True
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=True     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=" / "     ,SCREAMING_SNAKE_CASE__			:      Tuple=" // "     ,SCREAMING_SNAKE_CASE__			:      int=5     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=300     ,SCREAMING_SNAKE_CASE__			:      List[Any]=768     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=8     ,SCREAMING_SNAKE_CASE__			:      Any="wiki_dpr"     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="train"     ,SCREAMING_SNAKE_CASE__			:      int="compressed"     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=False     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Tuple=0.0     ,SCREAMING_SNAKE_CASE__			:      int=True     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=False     ,SCREAMING_SNAKE_CASE__			:      Tuple=False     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=False     ,SCREAMING_SNAKE_CASE__			:      List[str]=True     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,**SCREAMING_SNAKE_CASE__			:      Optional[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"
     SCREAMING_SNAKE_CASE:List[Any]						      =					kwargs.pop("question_encoder"							)
     SCREAMING_SNAKE_CASE:str						      =					question_encoder_config.pop("model_type"							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					kwargs.pop("generator"							)
     SCREAMING_SNAKE_CASE:List[str]						      =					decoder_config.pop("model_type"							)

     from ..auto.configuration_auto import AutoConfig

     SCREAMING_SNAKE_CASE:int						      =					AutoConfig.for_model(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					AutoConfig.for_model(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					reduce_loss
     SCREAMING_SNAKE_CASE:List[Any]						      =					label_smoothing
     SCREAMING_SNAKE_CASE:int						      =					exclude_bos_score
     SCREAMING_SNAKE_CASE:Any						      =					do_marginalize

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					title_sep
     SCREAMING_SNAKE_CASE:Tuple						      =					doc_sep
     SCREAMING_SNAKE_CASE:Tuple						      =					n_docs
     SCREAMING_SNAKE_CASE:Dict						      =					max_combined_length

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

     SCREAMING_SNAKE_CASE:Any						      =					retrieval_vector_size
     SCREAMING_SNAKE_CASE:List[Any]						      =					retrieval_batch_size
     SCREAMING_SNAKE_CASE:int						      =					passages_path
     SCREAMING_SNAKE_CASE:List[Any]						      =					index_path
     SCREAMING_SNAKE_CASE:List[str]						      =					use_dummy_dataset

     SCREAMING_SNAKE_CASE:List[str]						      =					output_retrieved

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_deduplication

     SCREAMING_SNAKE_CASE:Dict						      =					use_cache

     if self.forced_eos_token_id is None:
       SCREAMING_SNAKE_CASE:int						      =					getattr(self.generator     ,"forced_eos_token_id"     ,SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      int							):
     return cls(question_encoder=question_encoder_config.to_dict()     ,generator=generator_config.to_dict()     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Dict						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:int						      =					self.question_encoder.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.generator.to_dict()
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.__class__.model_type
     return output









'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






