

"""simple docstring"""
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(__lowercase					)
class 	lowerCamelCase     (    __lowercase					):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE 	=   "rag"
  SCREAMING_SNAKE_CASE 	=   True



  def __init__(self				,    _lowerCamelCase=None				,    _lowerCamelCase=True				,    _lowerCamelCase=None				,    _lowerCamelCase=None				,    _lowerCamelCase=None				,    _lowerCamelCase=None				,    _lowerCamelCase=None				,    _lowerCamelCase=" / "				,    _lowerCamelCase=" // "				,    _lowerCamelCase=5				,    _lowerCamelCase=300				,    _lowerCamelCase=768				,    _lowerCamelCase=8				,    _lowerCamelCase="wiki_dpr"				,    _lowerCamelCase="train"				,    _lowerCamelCase="compressed"				,    _lowerCamelCase=None				,    _lowerCamelCase=None				,    _lowerCamelCase=False				,    _lowerCamelCase=False				,    _lowerCamelCase=0.0				,    _lowerCamelCase=True				,    _lowerCamelCase=False				,    _lowerCamelCase=False				,    _lowerCamelCase=False				,    _lowerCamelCase=True				,    _lowerCamelCase=None				,    **_lowerCamelCase				,    ):


        """simple docstring"""

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

        from ..auto.configuration_auto import AutoConfig

        UpperCAmelCase__       :				Optional[Any]						 =  AutoConfig.for_model(_a				,    **_a     )
        UpperCAmelCase__       :				Tuple						 =  AutoConfig.for_model(_a				,    **_a     )

        UpperCAmelCase__       :				List[Any]						 =  reduce_loss
        UpperCAmelCase__       :				Optional[Any]						 =  label_smoothing
        UpperCAmelCase__       :				Optional[Any]						 =  exclude_bos_score
        UpperCAmelCase__       :				Tuple						 =  do_marginalize

        UpperCAmelCase__       :				Optional[int]						 =  title_sep
        UpperCAmelCase__       :				Dict						 =  doc_sep
        UpperCAmelCase__       :				List[str]						 =  n_docs
        UpperCAmelCase__       :				Tuple						 =  max_combined_length

        UpperCAmelCase__       :				Union[str, Any]						 =  dataset
        UpperCAmelCase__       :				Optional[Any]						 =  dataset_split
        UpperCAmelCase__       :				List[str]						 =  index_name

        UpperCAmelCase__       :				Any						 =  retrieval_vector_size
        UpperCAmelCase__       :				str						 =  retrieval_batch_size
        UpperCAmelCase__       :				List[str]						 =  passages_path
        UpperCAmelCase__       :				int						 =  index_path
        UpperCAmelCase__       :				Optional[int]						 =  use_dummy_dataset

        UpperCAmelCase__       :				Union[str, Any]						 =  output_retrieved

        UpperCAmelCase__       :				Any						 =  do_deduplication

        UpperCAmelCase__       :				Dict						 =  use_cache

        if self.forced_eos_token_id is None:
              UpperCAmelCase__       :				List[Any]						 =  getattr(self.generator				,    """forced_eos_token_id"""				,    _a     )



  @classmethod
  def       _a	(cls				,    _lowerCamelCase				,    _lowerCamelCase				,    **_lowerCamelCase     ):


        """simple docstring"""

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




  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				Any						 =  copy.deepcopy(self.__dict__     )
        UpperCAmelCase__       :				Optional[Any]						 =  self.question_encoder.to_dict()
        UpperCAmelCase__       :				List[Any]						 =  self.generator.to_dict()
        UpperCAmelCase__       :				Union[str, Any]						 =  self.__class__.model_type
        return output




from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



