


import copy

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


lowerCamelCase__							:						Optional[int]							     =   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(lowercase_				)
class 		__magic_name__						(lowercase_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       'rag'
     __lowercase				:							Any	   =       True

     def __init__(					self:str     ,		_a:Optional[int]=None     ,		_a:List[str]=True     ,		_a:List[str]=None     ,		_a:Dict=None     ,		_a:List[Any]=None     ,		_a:Tuple=None     ,		_a:Dict=None     ,		_a:Optional[Any]=" / "     ,		_a:Optional[int]=" // "     ,		_a:Optional[int]=5     ,		_a:List[str]=3_00     ,		_a:List[Any]=7_68     ,		_a:Tuple=8     ,		_a:Dict="wiki_dpr"     ,		_a:Dict="train"     ,		_a:str="compressed"     ,		_a:int=None     ,		_a:Union[str, Any]=None     ,		_a:Optional[Any]=False     ,		_a:Optional[int]=False     ,		_a:List[Any]=0.0     ,		_a:Tuple=True     ,		_a:Optional[int]=False     ,		_a:Optional[Any]=False     ,		_a:Union[str, Any]=False     ,		_a:Optional[Any]=True     ,		_a:List[Any]=None     ,		**_a:List[Any]     ,		):
       super().__init__(
           bos_token_id=_lowercase     ,		pad_token_id=_lowercase     ,		eos_token_id=_lowercase     ,		decoder_start_token_id=_lowercase     ,		forced_eos_token_id=_lowercase     ,		is_encoder_decoder=_lowercase     ,		prefix=_lowercase     ,		vocab_size=_lowercase     ,		**_lowercase     ,		)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       snake_case__						= kwargs.pop('''question_encoder'''   )
       snake_case__						= question_encoder_config.pop('''model_type'''   )
       snake_case__						= kwargs.pop('''generator'''   )
       snake_case__						= decoder_config.pop('''model_type'''   )

       from ..auto.configuration_auto import AutoConfig

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

       snake_case__						= reduce_loss
       snake_case__						= label_smoothing
       snake_case__						= exclude_bos_score
       snake_case__						= do_marginalize

       snake_case__						= title_sep
       snake_case__						= doc_sep
       snake_case__						= n_docs
       snake_case__						= max_combined_length

       snake_case__						= dataset
       snake_case__						= dataset_split
       snake_case__						= index_name

       snake_case__						= retrieval_vector_size
       snake_case__						= retrieval_batch_size
       snake_case__						= passages_path
       snake_case__						= index_path
       snake_case__						= use_dummy_dataset

       snake_case__						= output_retrieved

       snake_case__						= do_deduplication

       snake_case__						= use_cache

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

     @classmethod
     def 	SCREAMING_SNAKE_CASE__						(					cls:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:List[Any]     ,		**_a:Union[str, Any]   ):
       return cls(question_encoder=question_encoder_config.to_dict()     ,		generator=generator_config.to_dict()     ,		**_lowercase   )







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= copy.deepcopy(self.__dict__   )
       snake_case__						= self.question_encoder.to_dict()
       snake_case__						= self.generator.to_dict()
       snake_case__						= self.__class__.model_type
       return output




'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





