



def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							Dict						    = []
						A__	:							Optional[int]						    = set({"(", "[", "{"}       )
						A__	:							Tuple						    = set({")", "]", "}"}       )
						A__	:							int						    = {"{": "}", "[": "]", "(": ")"}

						for i in range(len(__lowercase       )       ):
												if s[i] in open_brackets:
																		stack.append(s[i]       )

												elif s[i] in closed_brackets and (
												    len(__lowercase       ) == 0 or (len(__lowercase       ) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(__lowercase       ) == 0
def 	snake_case__       (					)		->		str:







						"""simple docstring"""

						A__	:							Optional[int]						    = input("Enter sequence of brackets: "       )
						if is_balanced(__lowercase       ):
												print(__lowercase							,     "is balanced"       )
						else:
												print(__lowercase							,     "is not balanced"       )


if __name__ == "__main__":
		main()










import logging
import random

import ray

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


snake_case    :						Any  =						logging.getLogger(__name__)




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]):
											A__	:							Dict						    = False







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if not self.initialized:
																	A__	:							List[Any]						    = RagRetriever(
																	    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
																	A__	:							Dict						    = True







					def  _lowercase			(       self      :     Union[str, Any]):
											self.retriever.index.init_index()







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[int]			,		_A      :     List[str]):
											A__    ,       A__	:							str						    = self.retriever._main_retrieve(_A			,		_A)
											return doc_ids, retrieved_doc_embeds




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Union[str, Any]			,		_A      :     Tuple			,		_A      :     int			,		_A      :     Tuple			,		_A      :     Union[str, Any]=None):
											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__	:							str						    = 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  _lowercase			(       self      :     Optional[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  _lowercase			(       self      :     Union[str, Any]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if len(self.retrieval_workers) > 0:
																	# Select a random retrieval actor.
																	A__	:							Optional[Any]						    = self.retrieval_workers[random.randint(0			,		len(self.retrieval_workers) - 1)]
																	A__    ,       A__	:							List[Any]						    = ray.get(random_worker.retrieve.remote(_A			,		_A))
											else:
																	A__    ,       A__	:							Union[str, Any]						    = self._main_retrieve(_A			,		_A)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A)







					@classmethod
					def  _lowercase			(       cls      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											return super(_A			,		cls).get_tokenizers(_A			,		_A			,		**_A)







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