

import copy

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


a_     :    Dict     		=   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(SCREAMING_SNAKE_CASE )
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    ="rag"
    __UpperCamelCase    =True

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

     """simple docstring"""






     super().__init__(
         bos_token_id=snake_case__				,     pad_token_id=snake_case__				,     eos_token_id=snake_case__				,     decoder_start_token_id=snake_case__				,     forced_eos_token_id=snake_case__				,     is_encoder_decoder=snake_case__				,     prefix=snake_case__				,     vocab_size=snake_case__				,     **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		    =      kwargs.pop('question_encoder'     )
     SCREAMING_SNAKE_CASE		    =      question_encoder_config.pop('model_type'     )
     SCREAMING_SNAKE_CASE		    =      kwargs.pop('generator'     )
     SCREAMING_SNAKE_CASE		    =      decoder_config.pop('model_type'     )

     from ..auto.configuration_auto import AutoConfig

     SCREAMING_SNAKE_CASE		    =      AutoConfig.for_model(snake_case__				,     **snake_case__     )
     SCREAMING_SNAKE_CASE		    =      AutoConfig.for_model(snake_case__				,     **snake_case__     )

     SCREAMING_SNAKE_CASE		    =      reduce_loss
     SCREAMING_SNAKE_CASE		    =      label_smoothing
     SCREAMING_SNAKE_CASE		    =      exclude_bos_score
     SCREAMING_SNAKE_CASE		    =      do_marginalize

     SCREAMING_SNAKE_CASE		    =      title_sep
     SCREAMING_SNAKE_CASE		    =      doc_sep
     SCREAMING_SNAKE_CASE		    =      n_docs
     SCREAMING_SNAKE_CASE		    =      max_combined_length

     SCREAMING_SNAKE_CASE		    =      dataset
     SCREAMING_SNAKE_CASE		    =      dataset_split
     SCREAMING_SNAKE_CASE		    =      index_name

     SCREAMING_SNAKE_CASE		    =      retrieval_vector_size
     SCREAMING_SNAKE_CASE		    =      retrieval_batch_size
     SCREAMING_SNAKE_CASE		    =      passages_path
     SCREAMING_SNAKE_CASE		    =      index_path
     SCREAMING_SNAKE_CASE		    =      use_dummy_dataset

     SCREAMING_SNAKE_CASE		    =      output_retrieved

     SCREAMING_SNAKE_CASE		    =      do_deduplication

     SCREAMING_SNAKE_CASE		    =      use_cache

     if self.forced_eos_token_id is None:
      SCREAMING_SNAKE_CASE		    =      getattr(self.generator				,     'forced_eos_token_id'				,     snake_case__     )

    @classmethod
    def UpperCamelCase      (      cls			:   str				,     snake_case__			:   PretrainedConfig				,     snake_case__			:   PretrainedConfig				,     **snake_case__			:   str     ):

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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




import string


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''
 for i in sequence:
  SCREAMING_SNAKE_CASE		    =      ord(_UpperCamelCase						)
  if 65 <= extract <= 90:
   output += chr(1_55 - extract						)
  elif 97 <= extract <= 1_22:
   output += chr(2_19 - extract						)
  else:
   output += i
 return output


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      string.ascii_letters
 SCREAMING_SNAKE_CASE		    =      string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(_UpperCamelCase						)] if c in letters else c for c in sequence						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 from timeit import timeit

 print('Running performance benchmarks...'						)
 SCREAMING_SNAKE_CASE		    =      'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)
 print(f""">      atbash(): {timeit('atbash(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)


if __name__ == "__main__":
      for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
            print(F"""{example} encrypted in atbash: {atbash(example)}""")
      benchmark()

