




'''simple docstring'''




import logging
import random

import ray

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


snake_case__          =						logging.getLogger(__name__)






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     Optional[int] =							False







						def       _a    (     self      :       Any							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       str							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if not self.initialized:
																A_ :     Optional[Any] =							RagRetriever(
																    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )
																A_ :     Optional[Any] =							True







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											self.retriever.index.init_index()







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											del self.retriever
											A_ :     List[Any] =							False







						def       _a    (     self      :       Optional[int]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ ,A_ :     Dict =							self.retriever._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
											A_ :     int =							self.retriever.index.get_doc_dicts(_lowerCamelCase    )
											return doc_ids, retrieved_doc_embeds, doc_dicts






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						def __init__(     self      :       int							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Tuple=None    ):





											"""simple docstring"""







											if index is not None and index.is_initialized() and len(_lowerCamelCase    ) > 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__(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )

											A_ :     Tuple =							retrieval_workers
											A_ :     Any =							question_encoder_tokenizer
											A_ :     Dict =							generator_tokenizer
											if len(self.retrieval_workers    ) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )
																        for worker in self.retrieval_workers
																    ]    )







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											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       _a    (     self      :       Dict							, _lowerCamelCase      :       Tuple							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







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







						@classmethod
						def       _a    (     cls      :       List[str]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Optional[Any]=None							, **_lowerCamelCase      :       str    ):





											"""simple docstring"""







											return super(_lowerCamelCase							, cls    ).get_tokenizers(_lowerCamelCase							, _lowerCamelCase							, **_lowerCamelCase    )







						@classmethod
						def       _a    (     cls      :       Optional[Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       List[Any]							, _lowerCamelCase      :       str=None							, **_lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											A_ :     Tuple =							kwargs.pop('''config'''							, _lowerCamelCase    ) or RagConfig.from_pretrained(_lowerCamelCase							, **_lowerCamelCase    )
											A_ :     List[Any] =							RagTokenizer.from_pretrained(_lowerCamelCase							, config=_lowerCamelCase    )
											A_ :     Union[str, Any] =							rag_tokenizer.question_encoder
											A_ :     Dict =							rag_tokenizer.generator

											if indexed_dataset is not None:
																A_ :     Any =							'''custom'''
																A_ :     int =							CustomHFIndex(config.retrieval_vector_size							, _lowerCamelCase    )
											else:
																A_ :     Optional[int] =							cls._build_index(_lowerCamelCase    )

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







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											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
											A_ :     Dict =							self._build_index(self.config    )

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









'''simple docstring'''




from __future__ import annotations

snake_case__          =						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Dict							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     int =							-1
											A_ :     int =							-1
											A_ :     list[int] =							[-1] * length
											A_ :     Node | None =							None
											A_ :     Node | None =							None







						def __repr__(     self      :       List[str]    ):





											"""simple docstring"""







											return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   Node | None:
					A_ :     Optional[Any] =							Node(len(lowerCamelCase__			)			)
					A_ ,A_ :     str =							min(lowerCamelCase__			), max(lowerCamelCase__			)
					# Leaf node case where the node contains only one unique value
					if root.minn == root.maxx:
										return root
					A_ :     Optional[int] =							(root.minn + root.maxx) // 2

					A_ :     list[int] =							[]
					A_ :     list[int] =							[]

					for index, num in enumerate(lowerCamelCase__			):
										if num <= pivot:
															left_arr.append(lowerCamelCase__			)
										else:
															right_arr.append(lowerCamelCase__			)
										A_ :     Union[str, Any] =							len(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					return root




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index < 0 or node is None:
										return 0
					# Leaf node cases
					if node.minn == node.maxx:
										return index + 1 if node.minn == num else 0
					A_ :     List[Any] =							(node.minn + node.maxx) // 2
					if num <= pivot:
										# go the left subtree and map index to the left subtree
										return rank_till_index(node.left			,		lowerCamelCase__			,		node.map_left[index] - 1			)
					else:
										# go to the right subtree and map index to the right subtree
										return rank_till_index(node.right			,		lowerCamelCase__			,		index - node.map_left[index]			)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if start > end:
										return 0
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		start - 1			)
					return rank_till_end - rank_before_start




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index > (end - start) or start > end or node is None:
										return -1
					# Leaf node case
					if node.minn == node.maxx:
										return node.minn
					# Number of elements in the left subtree in interval [start, end]
					A_ :     List[str] =							node.map_left[end] - (
					    node.map_left[start - 1] if start else 0
					)
					if num_elements_in_left_tree > index:
										return quantile(
										    node.left			,		lowerCamelCase__			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
					else:
										return quantile(
										    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if (
					    start > end
					    or node is None
					    or start_num > end_num
					    or node.minn > end_num
					    or node.maxx < start_num
					):
										return 0
					if start_num <= node.minn and node.maxx <= end_num:
										return end - start + 1
					A_ :     int =							range_counting(
					    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					A_ :     List[str] =							range_counting(
					    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



