





import copy

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


SCREAMING_SNAKE_CASE_		     = 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(lowercase_     )
class 	UpperCamelCase__				(   lowercase_     ):



					'''simple docstring'''

					__snake_case					:			List[str]				=    '''rag'''
					__snake_case					:			Tuple				=    True






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




											'''simple docstring'''


											super().__init__(
											    bos_token_id=UpperCamelCase__		,pad_token_id=UpperCamelCase__		,eos_token_id=UpperCamelCase__		,decoder_start_token_id=UpperCamelCase__		,forced_eos_token_id=UpperCamelCase__		,is_encoder_decoder=UpperCamelCase__		,prefix=UpperCamelCase__		,vocab_size=UpperCamelCase__		,**UpperCamelCase__		,)
											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(UpperCamelCase__		,**UpperCamelCase__ )
											SCREAMING_SNAKE_CASE	=		AutoConfig.for_model(UpperCamelCase__		,**UpperCamelCase__ )

											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"""		,UpperCamelCase__ )






					@classmethod
					def        SCREAMING_SNAKE_CASE__						(							cls		:					Dict		,lowerCamelCase__		:					PretrainedConfig		,lowerCamelCase__		:					PretrainedConfig		,**lowerCamelCase__		:					Tuple )  ->	List[str]:




											'''simple docstring'''


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






					def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Any:




											'''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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


