
"""simple docstring"""


import logging
import random

import ray

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


_lowercase							   =				logging.getLogger(__name__)







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Dict      )     ->   Tuple:
							A						   =   False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      int  ,A_     :      Any  ,A_     :      Dict  ,A_     :      Union[str, Any]      )     ->   Tuple:
							if not self.initialized:
								A						   =   RagRetriever(
								    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,index=A_  ,init_retrieval=A_  ,)
								A						   =   True


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   str:
							self.retriever.index.init_index()




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      int      )     ->   List[Any]:
							A   ,      A						   =   self.retriever._main_retrieve(A_  ,A_      )
							return doc_ids, retrieved_doc_embeds







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      int  ,A_     :      Any  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      str=None      )     ->   List[str]:
							if index is not None and index.is_initialized() and len(A_      ) > 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__(
							    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,index=A_  ,init_retrieval=A_  ,)
							A						   =   retrieval_workers
							if len(self.retrieval_workers      ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(A_  ,A_  ,A_  ,A_      )
								        for worker in self.retrieval_workers
								    ]      )


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


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]=None  ,**A_     :      Any      )     ->   str:
							return super(A_  ,cls      ).get_tokenizers(A_  ,A_  ,**A_      )




						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Any  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      int=None  ,**A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   kwargs.pop('config'  ,A_      ) or RagConfig.from_pretrained(A_  ,**A_      )
							A						   =   RagTokenizer.from_pretrained(A_  ,config=A_      )
							A						   =   rag_tokenizer.question_encoder
							A						   =   rag_tokenizer.generator
							if indexed_dataset is not None:
								A						   =   'custom'
								A						   =   CustomHFIndex(config.retrieval_vector_size  ,A_      )
							else:
								A						   =   cls._build_index(A_      )
							return cls(
							    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,retrieval_workers=A_  ,index=A_  ,)

"""simple docstring"""


def       _snake_case				(		snake_case__   :     list	,      snake_case__   :     list	,      snake_case__   :     int     ):
	A						   =   len(snake_case__     )
	A						   =   [[0] * n for i in range(snake_case__     )]
	for i in range(snake_case__     ):
		A						   =   y_points[i]

	for i in range(2	,      snake_case__     ):
		for j in range(snake_case__	,      snake_case__     ):
			A						   =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()