



import copy

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


__a     =			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(__snake_case  )
class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''rag'''
      UpperCamelCase								=       True





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






         """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"
         UpperCAmelCase							=     kwargs.pop("""question_encoder"""       )
         UpperCAmelCase							=     question_encoder_config.pop("""model_type"""       )
         UpperCAmelCase							=     kwargs.pop("""generator"""       )
         UpperCAmelCase							=     decoder_config.pop("""model_type"""       )

         from ..auto.configuration_auto import AutoConfig

         UpperCAmelCase							=     AutoConfig.for_model(__lowerCamelCase					,       **__lowerCamelCase       )
         UpperCAmelCase							=     AutoConfig.for_model(__lowerCamelCase					,       **__lowerCamelCase       )

         UpperCAmelCase							=     reduce_loss
         UpperCAmelCase							=     label_smoothing
         UpperCAmelCase							=     exclude_bos_score
         UpperCAmelCase							=     do_marginalize

         UpperCAmelCase							=     title_sep
         UpperCAmelCase							=     doc_sep
         UpperCAmelCase							=     n_docs
         UpperCAmelCase							=     max_combined_length

         UpperCAmelCase							=     dataset
         UpperCAmelCase							=     dataset_split
         UpperCAmelCase							=     index_name

         UpperCAmelCase							=     retrieval_vector_size
         UpperCAmelCase							=     retrieval_batch_size
         UpperCAmelCase							=     passages_path
         UpperCAmelCase							=     index_path
         UpperCAmelCase							=     use_dummy_dataset

         UpperCAmelCase							=     output_retrieved

         UpperCAmelCase							=     do_deduplication

         UpperCAmelCase							=     use_cache

         if self.forced_eos_token_id is None:
            UpperCAmelCase							=     getattr(self.generator					,       """forced_eos_token_id"""					,       __lowerCamelCase       )





      @classmethod
      def        _lowercase				(		cls						:					Any					,       __lowerCamelCase						:					PretrainedConfig					,       __lowerCamelCase						:					PretrainedConfig					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   PretrainedConfig:






         """simple docstring"""







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





      def        _lowercase				(		self						:					List[str]       )    ->   Tuple:






         """simple docstring"""







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







from math import sqrt
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0_0_0_0_0_0   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   UpperCAmelCase							=     42

   while num_cuboids <= limit:
      max_cuboid_size += 1
      for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1   ):
         if sqrt(sum_shortest_sides**2 + max_cuboid_size**2   ).is_integer():
            num_cuboids += (
                min(lowerCAmelCase_  ,						sum_shortest_sides // 2   )
                - max(1  ,						sum_shortest_sides - max_cuboid_size   )
                + 1
            )

   return max_cuboid_size


if __name__ == "__main__":
 print(F"""{solution() = }""")



