
'''simple docstring'''







import logging
import random

import ray

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


_lowercase				:						Tuple												= logging.getLogger(__name__)






class 				lowerCAmelCase__     :






					def __init__(     self	):


											"""simple docstring"""





											lowercase_			:  str									= False






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if not self.initialized:
																	lowercase_			:  int									= RagRetriever(
																	    __SCREAMING_SNAKE_CASE       ,							question_encoder_tokenizer=__SCREAMING_SNAKE_CASE       ,							generator_tokenizer=__SCREAMING_SNAKE_CASE       ,							index=__SCREAMING_SNAKE_CASE       ,							init_retrieval=__SCREAMING_SNAKE_CASE       ,							)
																	lowercase_			:  Union[str, Any]									= True






					def 			_snake_case					(     self	):


											"""simple docstring"""





											self.retriever.index.init_index()






					def 			_snake_case					(     self	):


											"""simple docstring"""





											del self.retriever
											lowercase_			:  Dict									= False






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_		,				lowercase_			:  Any									= self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[int]									= self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE	)
											return doc_ids, retrieved_doc_embeds, doc_dicts







class 				lowerCAmelCase__     (						lowerCamelCase_  ):






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


											"""simple docstring"""





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

											lowercase_			:  List[Any]									= retrieval_workers
											lowercase_			:  Optional[Any]									= question_encoder_tokenizer
											lowercase_			:  Union[str, Any]									= generator_tokenizer
											if len(self.retrieval_workers	) > 0:
																	ray.get(
																	    [
																	        worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
																	        for worker in self.retrieval_workers
																	    ]	)






					def 			_snake_case					(     self	):


											"""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 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					@classmethod
					def 			_snake_case					(     cls       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					@classmethod
					def 			_snake_case					(     cls       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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

											if indexed_dataset is not None:
																	lowercase_			:  Optional[Any]									= '''custom'''
																	lowercase_			:  Dict									= CustomHFIndex(config.retrieval_vector_size       ,							__SCREAMING_SNAKE_CASE	)
											else:
																	lowercase_			:  List[Any]									= cls._build_index(__SCREAMING_SNAKE_CASE	)

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






					def 			_snake_case					(     self	):


											"""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
											lowercase_			:  int									= self._build_index(self.config	)

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





'''simple docstring'''







from itertools import combinations, product
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if xa == ya == 0 or xa == ya == 0:
												return False
						lowercase_			:  Optional[Any]									= xa * xa + ya * ya
						lowercase_			:  List[str]									= xa * xa + ya * ya
						lowercase_			:  Any									= (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 50							):



						"""simple docstring"""





						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1							)   ,   repeat=2							)   ,   2							)
						    if is_right(*__SCREAMING_SNAKE_CASE   ,   *__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							print(f"""{solution() = }""")



