import copy

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


UpperCamelCase__											=					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      A							(				_lowerCamelCase     ):
   __UpperCAmelCase : Dict      =			'rag'
   __UpperCAmelCase : Optional[int]      =			True


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						Dict=True       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  __UpperCAmelCase     :						int=None       ,  __UpperCAmelCase     :						Union[str, Any]=" / "       ,  __UpperCAmelCase     :						Any=" // "       ,  __UpperCAmelCase     :						Optional[Any]=5       ,  __UpperCAmelCase     :						Any=3_0_0       ,  __UpperCAmelCase     :						List[str]=7_6_8       ,  __UpperCAmelCase     :						Optional[int]=8       ,  __UpperCAmelCase     :						Union[str, Any]="wiki_dpr"       ,  __UpperCAmelCase     :						int="train"       ,  __UpperCAmelCase     :						Any="compressed"       ,  __UpperCAmelCase     :						int=None       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						List[Any]=False       ,  __UpperCAmelCase     :						Any=False       ,  __UpperCAmelCase     :						List[str]=0.0       ,  __UpperCAmelCase     :						Any=True       ,  __UpperCAmelCase     :						Tuple=False       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						int=False       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						List[Any]=None       ,  **__UpperCAmelCase     :						List[str]       ,  )						->				List[Any]:
         """simple docstring"""



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

         from ..auto.configuration_auto import AutoConfig

         UpperCAmelCase__								=  AutoConfig.for_model(_UpperCamelCase       ,  **_UpperCamelCase		)
         UpperCAmelCase__								=  AutoConfig.for_model(_UpperCamelCase       ,  **_UpperCamelCase		)

         UpperCAmelCase__								=  reduce_loss
         UpperCAmelCase__								=  label_smoothing
         UpperCAmelCase__								=  exclude_bos_score
         UpperCAmelCase__								=  do_marginalize

         UpperCAmelCase__								=  title_sep
         UpperCAmelCase__								=  doc_sep
         UpperCAmelCase__								=  n_docs
         UpperCAmelCase__								=  max_combined_length

         UpperCAmelCase__								=  dataset
         UpperCAmelCase__								=  dataset_split
         UpperCAmelCase__								=  index_name

         UpperCAmelCase__								=  retrieval_vector_size
         UpperCAmelCase__								=  retrieval_batch_size
         UpperCAmelCase__								=  passages_path
         UpperCAmelCase__								=  index_path
         UpperCAmelCase__								=  use_dummy_dataset

         UpperCAmelCase__								=  output_retrieved

         UpperCAmelCase__								=  do_deduplication

         UpperCAmelCase__								=  use_cache

         if self.forced_eos_token_id is None:
               UpperCAmelCase__								=  getattr(self.generator       ,  "forced_eos_token_id"       ,  _UpperCamelCase		)


   @classmethod
   def 			lowercase_      (cls     :						List[str]       ,  __UpperCAmelCase     :						PretrainedConfig       ,  __UpperCAmelCase     :						PretrainedConfig       ,  **__UpperCAmelCase     :						int		)						->				PretrainedConfig:
         """simple docstring"""



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





   def 			lowercase_      (self     :						List[Any]		)						->				int:
         """simple docstring"""



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










from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





