
"""simple docstring"""




from maths.prime_check import is_prime



def 							_lowerCamelCase			(			__a     ):
		if not isinstance(__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			F'Input value of [number={number}] must be an integer'
				raise TypeError(__a     )
		if is_prime(__a     ) and is_prime(number + 2     ):
				return number + 2
		else:
				return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

"""simple docstring"""




import copy

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


lowerCAmelCase__				=					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 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''rag'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=" / "							,							SCREAMING_SNAKE_CASE_=" // "							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=3_00							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=8							,							SCREAMING_SNAKE_CASE_="wiki_dpr"							,							SCREAMING_SNAKE_CASE_="train"							,							SCREAMING_SNAKE_CASE_="compressed"							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									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_    			=			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(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_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'''							,							SCREAMING_SNAKE_CASE_     )

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""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