




'''simple docstring'''


import copy

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


_lowercase  =			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(UpperCAmelCase__						)
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Optional[int]			  =      '''rag'''
	lowercase_       :     Any			  =      True




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=None       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  Dict=None       ,		__lowerCAmelCase     :  int=None       ,		__lowerCAmelCase     :  str=None       ,		__lowerCAmelCase     :  Optional[int]=" / "       ,		__lowerCAmelCase     :  str=" // "       ,		__lowerCAmelCase     :  int=5       ,		__lowerCAmelCase     :  str=3_0_0       ,		__lowerCAmelCase     :  Any=7_6_8       ,		__lowerCAmelCase     :  Any=8       ,		__lowerCAmelCase     :  Union[str, Any]="wiki_dpr"       ,		__lowerCAmelCase     :  Tuple="train"       ,		__lowerCAmelCase     :  Dict="compressed"       ,		__lowerCAmelCase     :  Dict=None       ,		__lowerCAmelCase     :  Optional[int]=None       ,		__lowerCAmelCase     :  Tuple=False       ,		__lowerCAmelCase     :  Union[str, Any]=False       ,		__lowerCAmelCase     :  Union[str, Any]=0.0       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=False       ,		__lowerCAmelCase     :  Any=False       ,		__lowerCAmelCase     :  Union[str, Any]=False       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  List[str]=None       ,		**__lowerCAmelCase     :  Union[str, Any]       ,		):
							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"
							__snake_case						 =   kwargs.pop('question_encoder'      )
							__snake_case						 =   question_encoder_config.pop('model_type'      )
							__snake_case						 =   kwargs.pop('generator'      )
							__snake_case						 =   decoder_config.pop('model_type'      )

							from ..auto.configuration_auto import AutoConfig

							__snake_case						 =   AutoConfig.for_model(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   AutoConfig.for_model(__lowerCAmelCase       ,		**__lowerCAmelCase      )

							__snake_case						 =   reduce_loss
							__snake_case						 =   label_smoothing
							__snake_case						 =   exclude_bos_score
							__snake_case						 =   do_marginalize

							__snake_case						 =   title_sep
							__snake_case						 =   doc_sep
							__snake_case						 =   n_docs
							__snake_case						 =   max_combined_length

							__snake_case						 =   dataset
							__snake_case						 =   dataset_split
							__snake_case						 =   index_name

							__snake_case						 =   retrieval_vector_size
							__snake_case						 =   retrieval_batch_size
							__snake_case						 =   passages_path
							__snake_case						 =   index_path
							__snake_case						 =   use_dummy_dataset

							__snake_case						 =   output_retrieved

							__snake_case						 =   do_deduplication

							__snake_case						 =   use_cache

							if self.forced_eos_token_id is None:
													__snake_case						 =   getattr(self.generator       ,		'forced_eos_token_id'       ,		__lowerCAmelCase      )




	@classmethod
	def   lowercase__   (       cls     :  List[str]       ,		__lowerCAmelCase     :  PretrainedConfig       ,		__lowerCAmelCase     :  PretrainedConfig       ,		**__lowerCAmelCase     :  List[str]      ):
							return cls(question_encoder=question_encoder_config.to_dict()       ,		generator=generator_config.to_dict()       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   copy.deepcopy(self.__dict__      )
							__snake_case						 =   self.question_encoder.to_dict()
							__snake_case						 =   self.generator.to_dict()
							__snake_case						 =   self.__class__.model_type
							return output







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

