






import logging
import random

import ray

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


__UpperCAmelCase							:   Dict							=  logging.getLogger(__name__)


class 					_snake_case						:






	def __init__(		self       )						->    Optional[int]:
					snake_case__      :List[Any]         =   False






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					if not self.initialized:
									snake_case__      :Dict         =   RagRetriever(
									    UpperCamelCase					,question_encoder_tokenizer=UpperCamelCase					,generator_tokenizer=UpperCamelCase					,index=UpperCamelCase					,init_retrieval=UpperCamelCase					,)
									snake_case__      :List[Any]         =   True






	def    lowerCAmelCase_							(		self       )						->    int:
					self.retriever.index.init_index()






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					snake_case__    ,						snake_case__      :List[Any]         =   self.retriever._main_retrieve(UpperCamelCase					,UpperCamelCase       )
					return doc_ids, retrieved_doc_embeds




class 					_snake_case						(			_A ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None       )						->    Dict:
					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					,)
					snake_case__      :Union[str, Any]         =   retrieval_workers
					if len(self.retrieval_workers       ) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )
									        for worker in self.retrieval_workers
									    ]       )






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






	@classmethod
	def    lowerCAmelCase_							(		cls					,UpperCamelCase					,UpperCamelCase=None					,**UpperCamelCase       )						->    str:
					return super(UpperCamelCase					,cls       ).get_tokenizers(UpperCamelCase					,UpperCamelCase					,**UpperCamelCase       )






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







from __future__ import annotations


class 					_snake_case						:






	def __init__(		self					,UpperCamelCase       )						->    None:
					snake_case__      :Union[str, Any]         =   data
					snake_case__      :Node | None         =   None
					snake_case__      :Node | None         =   None





def 					lowercase_ (    __snake_case     :							Node | None      )     -> None:  # In Order traversal of the tree





				'''simple docstring'''


				if tree:
								display(tree.left      )
								print(tree.data      )
								display(tree.right      )





def 					lowercase_ (    __snake_case     :							Node | None      )     -> int:





				'''simple docstring'''


				return 1 + max(depth_of_tree(tree.left      )	,	depth_of_tree(tree.right      )      ) if tree else 0





def 					lowercase_ (    __snake_case     :							Node      )     -> bool:





				'''simple docstring'''


				if not tree:
								return True
				if tree.left and tree.right:
								return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
				else:
								return not tree.left and not tree.right





def 					lowercase_ (    )     -> None:  # Main function for testing.





				'''simple docstring'''


				snake_case__      :Dict         =   Node(1      )
				snake_case__      :int         =   Node(2      )
				snake_case__      :Optional[Any]         =   Node(3      )
				snake_case__      :Tuple         =   Node(4      )
				snake_case__      :str         =   Node(5      )
				snake_case__      :Optional[Any]         =   Node(6      )
				snake_case__      :List[Any]         =   Node(7      )
				snake_case__      :List[str]         =   Node(8      )
				snake_case__      :Tuple         =   Node(9      )

				print(is_full_binary_tree(__snake_case      )      )
				print(depth_of_tree(__snake_case      )      )
				print("Tree is: "      )
				display(__snake_case      )


if __name__ == "__main__":
	main()