






'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"rag"
			snake_case__  =				True






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						__SCREAMING_SNAKE_CASE      :			int=None  ,						__SCREAMING_SNAKE_CASE      :			str=None  ,						__SCREAMING_SNAKE_CASE      :			Tuple=" / "  ,						__SCREAMING_SNAKE_CASE      :			Any=" // "  ,						__SCREAMING_SNAKE_CASE      :			List[str]=5  ,						__SCREAMING_SNAKE_CASE      :			Dict=300  ,						__SCREAMING_SNAKE_CASE      :			Dict=768  ,						__SCREAMING_SNAKE_CASE      :			Any=8  ,						__SCREAMING_SNAKE_CASE      :			int="wiki_dpr"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]="train"  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]="compressed"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			int=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=0.0  ,						__SCREAMING_SNAKE_CASE      :			str=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						__SCREAMING_SNAKE_CASE      :			str=False  ,						__SCREAMING_SNAKE_CASE      :			Any=True  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			List[Any]:
							super().__init__(
							    bos_token_id=__SCREAMING_SNAKE_CASE  ,						pad_token_id=__SCREAMING_SNAKE_CASE  ,						eos_token_id=__SCREAMING_SNAKE_CASE  ,						decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,						forced_eos_token_id=__SCREAMING_SNAKE_CASE  ,						is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,						prefix=__SCREAMING_SNAKE_CASE  ,						vocab_size=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)
							assert (
							    "question_encoder" in kwargs and "generator" in kwargs
							), "Config has to be initialized with question_encoder and generator config"
							a_		:  Any											=      kwargs.pop('''question_encoder'''					)
							a_		:  Dict											=      question_encoder_config.pop('''model_type'''					)
							a_		:  Optional[int]											=      kwargs.pop('''generator'''					)
							a_		:  str											=      decoder_config.pop('''model_type'''					)

							from ..auto.configuration_auto import AutoConfig

							a_		:  List[str]											=      AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      reduce_loss
							a_		:  Any											=      label_smoothing
							a_		:  Any											=      exclude_bos_score
							a_		:  Union[str, Any]											=      do_marginalize

							a_		:  Dict											=      title_sep
							a_		:  Union[str, Any]											=      doc_sep
							a_		:  Optional[Any]											=      n_docs
							a_		:  Tuple											=      max_combined_length

							a_		:  Dict											=      dataset
							a_		:  List[str]											=      dataset_split
							a_		:  int											=      index_name

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

							a_		:  Optional[int]											=      output_retrieved

							a_		:  Any											=      do_deduplication

							a_		:  str											=      use_cache

							if self.forced_eos_token_id is None:
											a_		:  Optional[Any]											=      getattr(self.generator  ,						'''forced_eos_token_id'''  ,						__SCREAMING_SNAKE_CASE					)






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			PretrainedConfig:
							return cls(question_encoder=question_encoder_config.to_dict()  ,						generator=generator_config.to_dict()  ,						**__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Any:
							a_		:  List[str]											=      copy.deepcopy(self.__dict__					)
							a_		:  List[str]											=      self.question_encoder.to_dict()
							a_		:  str											=      self.generator.to_dict()
							a_		:  Dict											=      self.__class__.model_type
							return output












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   tuple[int, int]						,		__A							:   int      ):
				a_				,			a_		:  List[str]											=      position
				a_		:  Optional[int]											=      [
				    (y + 1, x + 2),
				    (y - 1, x + 2),
				    (y + 1, x - 2),
				    (y - 1, x - 2),
				    (y + 2, x + 1),
				    (y + 2, x - 1),
				    (y - 2, x + 1),
				    (y - 2, x - 1),
				]
				a_		:  Any											=      []

				for position in positions:
								a_				,			a_		:  Dict											=      position
								if 0 <= y_test < n and 0 <= x_test < n:
												permissible_positions.append(__A      )

				return permissible_positions





def 		_UpperCAmelCase   (							__A							:   list[list[int]]      ):
				return not any(elem == 0 for row in board for elem in row      )





def 		_UpperCAmelCase   (							__A							:   list[list[int]]						,		__A							:   tuple[int, int]						,		__A							:   int      ):

				if is_complete(__A      ):
								return True

				for position in get_valid_pos(__A						,		len(__A      )      ):
								a_				,			a_		:  Dict											=      position

								if board[y][x] == 0:
												a_		:  Optional[Any]											=      curr + 1
												if open_knight_tour_helper(__A						,		__A						,		curr + 1      ):
																return True
												a_		:  Tuple											=      0

				return False





def 		_UpperCAmelCase   (							__A							:   int      ):
				a_		:  List[str]											=      [[0 for i in range(__A      )] for j in range(__A      )]

				for i in range(__A      ):
								for j in range(__A      ):
												a_		:  Optional[Any]											=      1
												if open_knight_tour_helper(__A						,		(i, j)						,		1      ):
																return board
												a_		:  Union[str, Any]											=      0

				a_		:  Dict											=      f'Open Kight Tour cannot be performed on a board of size {n}'
				raise ValueError(__A      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




