






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_					:   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_					:   Optional[Any]   =		ray.get(
                random_worker.retrieve.remote(_UpperCamelCase       ,	_UpperCamelCase     )     )
       else:
            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()






