




import logging
import random

import ray

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


__A						:		str          =						logging.getLogger(__name__)

class 		lowercase_       :




					def __init__(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  False




					def     _lowercase						(   self:		str,     _lowercase:		Tuple,     _lowercase:		List[str],     _lowercase:		List[str],     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									if not self.initialized:
													__lowerCAmelCase					=  RagRetriever(
													    _lowercase,     question_encoder_tokenizer=_lowercase,     generator_tokenizer=_lowercase,     index=_lowercase,     init_retrieval=_lowercase,     )
													__lowerCAmelCase					=  True




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									self.retriever.index.init_index()




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									del self.retriever
									__lowerCAmelCase					=  False







					def     _lowercase						(   self:		Any,     _lowercase:		Optional[int],     _lowercase:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.retriever._main_retrieve(_lowercase,     _lowercase)
									__lowerCAmelCase					=  self.retriever.index.get_doc_dicts(_lowercase)
									return doc_ids, retrieved_doc_embeds, doc_dicts





class 		lowercase_       (	lowerCAmelCase__				):




					def __init__(   self:		str,     _lowercase:		List[Any],     _lowercase:		str,     _lowercase:		Dict,     _lowercase:		List[str],     _lowercase:		str=None):


									'''simple docstring'''


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

									__lowerCAmelCase					=  retrieval_workers
									__lowerCAmelCase					=  question_encoder_tokenizer
									__lowerCAmelCase					=  generator_tokenizer
									if len(self.retrieval_workers) > 0:
													ray.get(
													    [
													        worker.create_rag_retriever.remote(_lowercase,     _lowercase,     _lowercase,     _lowercase)
													        for worker in self.retrieval_workers
													    ])




					def     _lowercase						(   self:		Tuple):


									'''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     _lowercase						(   self:		Optional[Any],     _lowercase:		Optional[Any],     _lowercase:		List[str]):


									'''simple docstring'''


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




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     _lowercase:		Optional[int],     _lowercase:		Any=None,     **_lowercase:		str):


									'''simple docstring'''


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




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     _lowercase:		List[Any],     _lowercase:		List[Any],     _lowercase:		List[str]=None,     **_lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  kwargs.pop("""config""",     _lowercase) or RagConfig.from_pretrained(_lowercase,     **_lowercase)
									__lowerCAmelCase					=  RagTokenizer.from_pretrained(_lowercase,     config=_lowercase)
									__lowerCAmelCase					=  rag_tokenizer.question_encoder
									__lowerCAmelCase					=  rag_tokenizer.generator

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

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







					def     _lowercase						(   self:		int):


									'''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
									__lowerCAmelCase					=  self._build_index(self.config)

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













"""simple docstring"""



import itertools
from pathlib import Path




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				[tuple(_SCREAMING_SNAKE_CASE			) for login in logins]

 UpperCAmelCase__ 						=				{char for login in split_logins for char in login}

 for permutation in itertools.permutations(_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__ 						=				True
  for login in logins:
   if not (
       permutation.index(login[0]			)
       < permutation.index(login[1]			)
       < permutation.index(login[2]			)
   ):
    UpperCAmelCase__ 						=				False
    break

  if satisfied:
   return int("""""".join(_SCREAMING_SNAKE_CASE			)			)

 raise Exception("""Unable to find the secret passcode"""			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE = "keylog.txt"			)    ->int:
 UpperCAmelCase__ 						=				Path(_SCREAMING_SNAKE_CASE			).parent.joinpath(_SCREAMING_SNAKE_CASE			).read_text().splitlines()

 return find_secret_passcode(_SCREAMING_SNAKE_CASE			)


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



