
'''simple docstring'''







import logging
import random

import ray

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


_snake_case					: str     			=					logging.getLogger(__name__)


class       lowerCAmelCase			:




		def __init__(					self   ):
								_SCREAMING_SNAKE_CASE					=			False




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								if not self.initialized:
														_SCREAMING_SNAKE_CASE					=			RagRetriever(
														    UpperCamelCase       ,    question_encoder_tokenizer=UpperCamelCase       ,    generator_tokenizer=UpperCamelCase       ,    index=UpperCamelCase       ,    init_retrieval=UpperCamelCase       ,    )
														_SCREAMING_SNAKE_CASE					=			True




		def      lowercase							(					self   ):
								self.retriever.index.init_index()




		def      lowercase							(					self   ):
								# delete the old self.retriever object before assigning the new index
								del self.retriever
								_SCREAMING_SNAKE_CASE					=			False




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.retriever._main_retrieve(UpperCamelCase       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			self.retriever.index.get_doc_dicts(UpperCamelCase   )
								return doc_ids, retrieved_doc_embeds, doc_dicts


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None   ):
								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       ,    )

								_SCREAMING_SNAKE_CASE					=			retrieval_workers
								_SCREAMING_SNAKE_CASE					=			question_encoder_tokenizer
								_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								if len(self.retrieval_workers   ) > 0:
														# Select a random retrieval actor.
														_SCREAMING_SNAKE_CASE					=			self.retrieval_workers[random.randint(0       ,    len(self.retrieval_workers   ) - 1   )]
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			ray.get(
														    random_worker.retrieve.remote(UpperCamelCase       ,    UpperCamelCase   )   )
								else:
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self._main_retrieve(UpperCamelCase       ,    UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			self.index.get_doc_dicts(UpperCamelCase   )
								return retrieved_doc_embeds, doc_ids, doc_dicts




		@classmethod
		def      lowercase							(					cls       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								return super(UpperCamelCase       ,    cls   ).get_tokenizers(UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase   )




		@classmethod
		def      lowercase							(					cls       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			kwargs.pop("config"       ,    UpperCamelCase   ) or RagConfig.from_pretrained(UpperCamelCase       ,    **UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			RagTokenizer.from_pretrained(UpperCamelCase       ,    config=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			rag_tokenizer.question_encoder
								_SCREAMING_SNAKE_CASE					=			rag_tokenizer.generator

								if indexed_dataset is not None:
														_SCREAMING_SNAKE_CASE					=			"custom"
														_SCREAMING_SNAKE_CASE					=			CustomHFIndex(config.retrieval_vector_size       ,    UpperCamelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			cls._build_index(UpperCamelCase   )

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




		def      lowercase							(					self   ):
								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
								_SCREAMING_SNAKE_CASE					=			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
								    ]   )

'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: int	):
						return (input_a, input_a).count(0	) % 2



def       _a		(						):
						assert xor_gate(0						,      0	) == 0
						assert xor_gate(0						,      1	) == 1
						assert xor_gate(1						,      0	) == 1
						assert xor_gate(1						,      1	) == 0


if __name__ == "__main__":
						print(xor_gate(0, 0))
						print(xor_gate(0, 1))