


'''simple docstring'''

import copy

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


_UpperCAmelCase				: Any							    =				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(__A      )
class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	'rag'
				__UpperCamelCase    :		Any	=	True




				def __init__(self       ,			__lowercase=None       ,			__lowercase=True       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=" / "       ,			__lowercase=" // "       ,			__lowercase=5       ,			__lowercase=3_00       ,			__lowercase=7_68       ,			__lowercase=8       ,			__lowercase="wiki_dpr"       ,			__lowercase="train"       ,			__lowercase="compressed"       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=0.0       ,			__lowercase=True       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=True       ,			__lowercase=None       ,			**__lowercase       ,			):
										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       ,			__lowercase       ,			__lowercase       ,			**__lowercase		):
										return cls(question_encoder=question_encoder_config.to_dict()       ,			generator=generator_config.to_dict()       ,			**__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				copy.deepcopy(self.__dict__		)
										__lowerCAmelCase							       =				self.question_encoder.to_dict()
										__lowerCAmelCase							       =				self.generator.to_dict()
										__lowerCAmelCase							       =				self.__class__.model_type
										return output





'''simple docstring'''

def 							__magic_name__(						):
						return [
						    a * b * (1_0_0_0 - a - b)
						    for a in range(1,				9_9_9)
						    for b in range(lowerCamelCase,				9_9_9)
						    if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
						][0]


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

