






import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase										=		logging.getLogger(__name__)



class 						lowerCamelCase   :







		'''simple docstring'''



		def __init__(							self     )			->							List[str]:
							UpperCAmelCase_					:   Optional[Any]   =		False



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							Union[str, Any]:
							if not self.initialized:
												UpperCAmelCase_					:   str   =		RagRetriever(
												    _UpperCamelCase       ,	question_encoder_tokenizer=_UpperCamelCase       ,	generator_tokenizer=_UpperCamelCase       ,	index=_UpperCamelCase       ,	init_retrieval=_UpperCamelCase       ,	)
												UpperCAmelCase_					:   Union[str, Any]   =		True



		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							self.retriever.index.init_index()



		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							UpperCAmelCase_					:   Optional[int]   =		False



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							int:
							UpperCAmelCase_ ,						UpperCAmelCase_					:   Union[str, Any]   =		self.retriever._main_retrieve(_UpperCamelCase       ,	_UpperCamelCase     )
							UpperCAmelCase_					:   int   =		self.retriever.index.get_doc_dicts(_UpperCamelCase     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase=None     )			->							str:
							if index is not None and index.is_initialized() and len(_UpperCamelCase     ) > 0:
												raise ValueError(
												    'When using Ray for distributed fine-tuning, '
												    'you\'ll need to provide the paths instead, '
												    'as the dataset and the index are loaded '
												    'separately. More info in examples/rag/use_own_knowledge_dataset.py '     )

							super().__init__(
							    _UpperCamelCase       ,	question_encoder_tokenizer=_UpperCamelCase       ,	generator_tokenizer=_UpperCamelCase       ,	index=_UpperCamelCase       ,	init_retrieval=_UpperCamelCase       ,	)

							UpperCAmelCase_					:   Union[str, Any]   =		retrieval_workers
							UpperCAmelCase_					:   Optional[Any]   =		question_encoder_tokenizer
							UpperCAmelCase_					:   List[Any]   =		generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
												ray.get(
												    [
												        worker.create_rag_retriever.remote(_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )
												        for worker in self.retrieval_workers
												    ]     )



		def       __UpperCAmelCase    (							self     )			->							Dict:
							logger.info('initializing retrieval'     )

							if len(self.retrieval_workers     ) > 0:
												ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
							else:
												# Non-distributed training. Load index into this same process.
												self.index.init_index()



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							Optional[Any]:
							if len(self.retrieval_workers     ) > 0:
												# Select a random retrieval actor.
												UpperCAmelCase_					:   int   =		self.retrieval_workers[random.randint(0       ,	len(self.retrieval_workers     ) - 1     )]
												UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[Any]   =		ray.get(
												    random_worker.retrieve.remote(_UpperCamelCase       ,	_UpperCamelCase     )     )
							else:
												UpperCAmelCase_ ,						UpperCAmelCase_					:   Tuple   =		self._main_retrieve(_UpperCamelCase       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   int   =		self.index.get_doc_dicts(_UpperCamelCase     )
							return retrieved_doc_embeds, doc_ids, doc_dicts



		@classmethod
		def       __UpperCAmelCase    (							cls       ,	_UpperCamelCase       ,	_UpperCamelCase=None       ,	**_UpperCamelCase     )			->							Dict:
							return super(_UpperCamelCase       ,	cls     ).get_tokenizers(_UpperCamelCase       ,	_UpperCamelCase       ,	**_UpperCamelCase     )



		@classmethod
		def       __UpperCAmelCase    (							cls       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase=None       ,	**_UpperCamelCase     )			->							int:
							UpperCAmelCase_					:   str   =		kwargs.pop('config'       ,	_UpperCamelCase     ) or RagConfig.from_pretrained(_UpperCamelCase       ,	**_UpperCamelCase     )
							UpperCAmelCase_					:   Optional[int]   =		RagTokenizer.from_pretrained(_UpperCamelCase       ,	config=_UpperCamelCase     )
							UpperCAmelCase_					:   int   =		rag_tokenizer.question_encoder
							UpperCAmelCase_					:   Dict   =		rag_tokenizer.generator

							if indexed_dataset is not None:
												UpperCAmelCase_					:   int   =		'custom'
												UpperCAmelCase_					:   Tuple   =		CustomHFIndex(config.retrieval_vector_size       ,	_UpperCamelCase     )
							else:
												UpperCAmelCase_					:   Tuple   =		cls._build_index(_UpperCamelCase     )

							return cls(
							    _UpperCamelCase       ,	question_encoder_tokenizer=_UpperCamelCase       ,	generator_tokenizer=_UpperCamelCase       ,	retrieval_workers=_UpperCamelCase       ,	index=_UpperCamelCase       ,	)



		def       __UpperCAmelCase    (							self     )			->							Optional[Any]:
							logger.info('re-loading the new dataset with embeddings'     )
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

							# build the index object again
							UpperCAmelCase_					:   List[Any]   =		self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config       ,	self.question_encoder_tokenizer       ,	self.generator_tokenizer       ,	_UpperCamelCase     )
							        for worker in self.retrieval_workers
							    ]     )














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






