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(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'rag'
 UpperCAmelCase_     			= True




 def __init__(      self :  Dict      ,   lowercase__ :  Any=None      ,   lowercase__ :  Tuple=True      ,   lowercase__ :  str=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Dict=" / "      ,   lowercase__ :  Optional[int]=" // "      ,   lowercase__ :  List[Any]=5      ,   lowercase__ :  List[Any]=300      ,   lowercase__ :  Optional[Any]=768      ,   lowercase__ :  Optional[int]=8      ,   lowercase__ :  str="wiki_dpr"      ,   lowercase__ :  Optional[Any]="train"      ,   lowercase__ :  Optional[int]="compressed"      ,   lowercase__ :  Any=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  Optional[int]=False      ,   lowercase__ :  Optional[Any]=False      ,   lowercase__ :  Optional[Any]=0.0      ,   lowercase__ :  str=True      ,   lowercase__ :  Any=False      ,   lowercase__ :  int=False      ,   lowercase__ :  Dict=False      ,   lowercase__ :  List[Any]=True      ,   lowercase__ :  List[str]=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"
        lowerCAmelCase__					     = kwargs.pop('question_encoder')
        lowerCAmelCase__					     = question_encoder_config.pop('model_type')
        lowerCAmelCase__					     = kwargs.pop('generator')
        lowerCAmelCase__					     = decoder_config.pop('model_type')

        from ..auto.configuration_auto import AutoConfig

        lowerCAmelCase__					     = AutoConfig.for_model(lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = AutoConfig.for_model(lowercase__      ,   **lowercase__)

        lowerCAmelCase__					     = reduce_loss
        lowerCAmelCase__					     = label_smoothing
        lowerCAmelCase__					     = exclude_bos_score
        lowerCAmelCase__					     = do_marginalize

        lowerCAmelCase__					     = title_sep
        lowerCAmelCase__					     = doc_sep
        lowerCAmelCase__					     = n_docs
        lowerCAmelCase__					     = max_combined_length

        lowerCAmelCase__					     = dataset
        lowerCAmelCase__					     = dataset_split
        lowerCAmelCase__					     = index_name

        lowerCAmelCase__					     = retrieval_vector_size
        lowerCAmelCase__					     = retrieval_batch_size
        lowerCAmelCase__					     = passages_path
        lowerCAmelCase__					     = index_path
        lowerCAmelCase__					     = use_dummy_dataset

        lowerCAmelCase__					     = output_retrieved

        lowerCAmelCase__					     = do_deduplication

        lowerCAmelCase__					     = use_cache

        if self.forced_eos_token_id is None:
               lowerCAmelCase__					     = getattr(self.generator      ,   'forced_eos_token_id'      ,   lowercase__)




 @classmethod
 def 		__snake_case						(      cls :  int      ,   lowercase__ :  PretrainedConfig      ,   lowercase__ :  PretrainedConfig      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = copy.deepcopy(self.__dict__)
        lowerCAmelCase__					     = self.question_encoder.to_dict()
        lowerCAmelCase__					     = self.generator.to_dict()
        lowerCAmelCase__					     = self.__class__.model_type
        return output

from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase__     						=			300  # TEMPERATURE (unit = K)






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  ):

       if donor_conc <= 0:
              raise ValueError('Donor concentration should be positive'     )
       elif acceptor_conc <= 0:
              raise ValueError('Acceptor concentration should be positive'     )
       elif intrinsic_conc <= 0:
              raise ValueError('Intrinsic concentration should be positive'     )
       elif donor_conc <= intrinsic_conc:
              raise ValueError(
                  'Donor concentration should be greater than intrinsic concentration'     )
       elif acceptor_conc <= intrinsic_conc:
              raise ValueError(
                  'Acceptor concentration should be greater than intrinsic concentration'     )
       else:
              return (
                  Boltzmann
                  * T
                  * log((donor_conc * acceptor_conc) / intrinsic_conc**2     )
                  / physical_constants["electron volt"][0]
              )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
