



import copy

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


lowerCamelCase__			= 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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Optional[int]    ='rag'
							__lowerCamelCase	:	int    =True





							def __init__(   self		:		List[str]      ,				__lowercase		:		Optional[int]=None      ,				__lowercase		:		Any=True      ,				__lowercase		:		Optional[Any]=None      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		Optional[int]=None      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		str=None      ,				__lowercase		:		Optional[int]=" / "      ,				__lowercase		:		Dict=" // "      ,				__lowercase		:		int=5      ,				__lowercase		:		Dict=300      ,				__lowercase		:		str=768      ,				__lowercase		:		Dict=8      ,				__lowercase		:		List[str]="wiki_dpr"      ,				__lowercase		:		Optional[int]="train"      ,				__lowercase		:		List[str]="compressed"      ,				__lowercase		:		Any=None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		int=False      ,				__lowercase		:		Union[str, Any]=False      ,				__lowercase		:		int=0.0      ,				__lowercase		:		int=True      ,				__lowercase		:		Any=False      ,				__lowercase		:		int=False      ,				__lowercase		:		Optional[Any]=False      ,				__lowercase		:		Dict=True      ,				__lowercase		:		Union[str, Any]=None      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											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"
											__a       =		kwargs.pop("""question_encoder"""	)
											__a       =		question_encoder_config.pop("""model_type"""	)
											__a       =		kwargs.pop("""generator"""	)
											__a       =		decoder_config.pop("""model_type"""	)

											from ..auto.configuration_auto import AutoConfig

											__a       =		AutoConfig.for_model(__lowercase      ,				**__lowercase	)
											__a       =		AutoConfig.for_model(__lowercase      ,				**__lowercase	)

											__a       =		reduce_loss
											__a       =		label_smoothing
											__a       =		exclude_bos_score
											__a       =		do_marginalize

											__a       =		title_sep
											__a       =		doc_sep
											__a       =		n_docs
											__a       =		max_combined_length

											__a       =		dataset
											__a       =		dataset_split
											__a       =		index_name

											__a       =		retrieval_vector_size
											__a       =		retrieval_batch_size
											__a       =		passages_path
											__a       =		index_path
											__a       =		use_dummy_dataset

											__a       =		output_retrieved

											__a       =		do_deduplication

											__a       =		use_cache

											if self.forced_eos_token_id is None:
															__a       =		getattr(self.generator      ,				"""forced_eos_token_id"""      ,				__lowercase	)





							@classmethod
							def 						UpperCamelCase_					(   cls		:		Dict      ,				__lowercase		:		PretrainedConfig      ,				__lowercase		:		PretrainedConfig      ,				**__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											return cls(question_encoder=question_encoder_config.to_dict()      ,				generator=generator_config.to_dict()      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		copy.deepcopy(self.__dict__	)
											__a       =		self.question_encoder.to_dict()
											__a       =		self.generator.to_dict()
											__a       =		self.__class__.model_type
											return output





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if index == number_of_items:
								return 0
				__a       =		0
				__a       =		0
				__a       =		knapsack(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     index + 1    )
				if weights[index] <= max_weight:
								__a       =		values[index] + knapsack(
								    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     max_weight - weights[index]				,     index + 1    )
				return max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
