



'''simple docstring'''




import copy

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


UpperCamelCase__       :	Dict    = R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''






@add_start_docstrings(lowerCamelCase__    )
class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		int			    =		'''rag'''
			_A      :		Dict			    =		True

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





					"""simple docstring"""






					super().__init__(
					    bos_token_id=lowerCAmelCase__							,     pad_token_id=lowerCAmelCase__							,     eos_token_id=lowerCAmelCase__							,     decoder_start_token_id=lowerCAmelCase__							,     forced_eos_token_id=lowerCAmelCase__							,     is_encoder_decoder=lowerCAmelCase__							,     prefix=lowerCAmelCase__							,     vocab_size=lowerCAmelCase__							,     **lowerCAmelCase__							,     )
					assert (
					    "question_encoder" in kwargs and "generator" in kwargs
					), "Config has to be initialized with question_encoder and generator config"
					__SCREAMING_SNAKE_CASE       :		Any           =		kwargs.pop("""question_encoder"""						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		question_encoder_config.pop("""model_type"""						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		kwargs.pop("""generator"""						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		decoder_config.pop("""model_type"""						)

					from ..auto.configuration_auto import AutoConfig

					__SCREAMING_SNAKE_CASE       :		List[str]           =		AutoConfig.for_model(lowerCAmelCase__							,     **lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		AutoConfig.for_model(lowerCAmelCase__							,     **lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		reduce_loss
					__SCREAMING_SNAKE_CASE       :		List[str]           =		label_smoothing
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		exclude_bos_score
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		do_marginalize

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		title_sep
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		doc_sep
					__SCREAMING_SNAKE_CASE       :		Any           =		n_docs
					__SCREAMING_SNAKE_CASE       :		Any           =		max_combined_length

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		dataset
					__SCREAMING_SNAKE_CASE       :		Dict           =		dataset_split
					__SCREAMING_SNAKE_CASE       :		Any           =		index_name

					__SCREAMING_SNAKE_CASE       :		Dict           =		retrieval_vector_size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		retrieval_batch_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		passages_path
					__SCREAMING_SNAKE_CASE       :		Tuple           =		index_path
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		use_dummy_dataset

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		output_retrieved

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		do_deduplication

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		use_cache

					if self.forced_eos_token_id is None:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		getattr(self.generator							,     """forced_eos_token_id"""							,     lowerCAmelCase__						)

			@classmethod
			def UpperCamelCase__					(							cls     : int							,     lowerCAmelCase__     : PretrainedConfig							,     lowerCAmelCase__     : PretrainedConfig							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






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



			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		copy.deepcopy(self.__dict__						)
					__SCREAMING_SNAKE_CASE       :		int           =		self.question_encoder.to_dict()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.generator.to_dict()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.__class__.model_type
					return output




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   float       ,      _lowerCamelCase:   list[float]					):
		if discount_rate < 0:
				raise ValueError("""Discount rate cannot be negative"""					)
		if not cash_flows:
				raise ValueError("""Cash flows list cannot be empty"""					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		sum(
		    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCamelCase					)					)
		return round(_lowerCamelCase       ,      ndigits=2					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()