import logging
import random

import ray

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


snake_case_						:			Union[str, Any]													=							logging.getLogger(__name__)

class 						A__							:




						def __init__(       self					:      str  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =False




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      Tuple				,    _a					:      List[str]				,    _a					:      List[str]  )							->     Dict:


										"""simple docstring"""





										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 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Optional[int]:


										"""simple docstring"""





										self.retriever.index.init_index()




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.retriever._main_retrieve(UpperCamelCase_				,    UpperCamelCase_  )
										return doc_ids, retrieved_doc_embeds

class 						A__							(		__lowerCamelCase  ):




						def __init__(       self					:      Union[str, Any]				,    _a					:      Tuple				,    _a					:      Any				,    _a					:      Union[str, Any]				,    _a					:      Optional[int]				,    _a					:      Any=None  )							->     Tuple:


										"""simple docstring"""





										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
										if len(self.retrieval_workers  ) > 0:
														ray.get(
														    [
														        worker.create_rag_retriever.remote(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_  )
														        for worker in self.retrieval_workers
														    ]  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[str]:


										"""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 							__UpperCamelCase				(       self					:      Dict				,    _a					:      str				,    _a					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										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			  =ray.get(random_worker.retrieve.remote(UpperCamelCase_				,    UpperCamelCase_  )  )
										else:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self._main_retrieve(UpperCamelCase_				,    UpperCamelCase_  )
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_  )




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Any				,    _a					:      List[str]				,    _a					:      Tuple=None				,    **_a					:      int  )							->     Tuple:


										"""simple docstring"""





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




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Optional[Any]				,    _a					:      int				,    _a					:      Tuple				,    _a					:      int=None				,    **_a					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





										_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_				,    )



snake_case_						:			dict[tuple[int, int, int], int]													=							{}







def 						lowerCamelCase(   a__	,a__	,a__):

				# if we are absent twice, or late 3 consecutive days,
				# no further prize strings are possible
				if late == 3 or absent == 2:
								return 0

				# if we have no days left, and have not failed any other rules,
				# we have a prize string
				if days == 0:
								return 1

				# No easy solution, so now we need to do the recursive calculation

				# First, check if the combination is already in the cache, and
				# if yes, return the stored value from there since we already
				# know the number of possible prize strings from this point on
				_SCREAMING_SNAKE_CASE			  =(days, absent, late)
				if key in cache:
								return cache[key]

				# now we calculate the three possible ways that can unfold from
				# this point on, depending on our attendance today

				# 1) if we are late (but not absent), the "absent" counter stays as
				# it is, but the "late" counter increases by one
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,late + 1)

				# 2) if we are absent, the "absent" counter increases by 1, and the
				# "late" counter resets to 0
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,absent + 1	,0)

				# 3) if we are on time, this resets the "late" counter and keeps the
				# absent counter
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,0)

				_SCREAMING_SNAKE_CASE			  =state_late + state_absent + state_ontime

				_SCREAMING_SNAKE_CASE			  =prizestrings
				return prizestrings







def 						lowerCamelCase(   a__ = 30):
				return _calculate(a__	,absent=0	,late=0)


if __name__ == "__main__":
							print(solution())