
"""simple docstring"""






import logging
import random

import ray

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


_lowercase							 =							logging.getLogger(__name__)

class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					False
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										if not self.initialized:
													__lowercase						      =					RagRetriever(
													    _lowerCamelCase     ,						question_encoder_tokenizer=_lowerCamelCase     ,						generator_tokenizer=_lowerCamelCase     ,						index=_lowerCamelCase     ,						init_retrieval=_lowerCamelCase     ,						)
													__lowercase						      =					True
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										self.retriever.index.init_index()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										# delete the old self.retriever object before assigning the new index
										del self.retriever
										__lowercase						      =					False





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase      ,				__lowercase						      =					self.retriever._main_retrieve(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					self.retriever.index.get_doc_dicts(_lowerCamelCase					)
										return doc_ids, retrieved_doc_embeds, doc_dicts





class 				__a    (   __a					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=None					) ->       str:




										'''simple docstring'''
										if index is not None and index.is_initialized() and len(_lowerCamelCase					) > 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__(
										    _lowerCamelCase     ,						question_encoder_tokenizer=_lowerCamelCase     ,						generator_tokenizer=_lowerCamelCase     ,						index=_lowerCamelCase     ,						init_retrieval=_lowerCamelCase     ,						)

										__lowercase						      =					retrieval_workers
										__lowercase						      =					question_encoder_tokenizer
										__lowercase						      =					generator_tokenizer
										if len(self.retrieval_workers					) > 0:
													ray.get(
													    [
													        worker.create_rag_retriever.remote(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
													        for worker in self.retrieval_workers
													    ]					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[int]:




										'''simple docstring'''
										if len(self.retrieval_workers					) > 0:
													# Select a random retrieval actor.
													__lowercase						      =					self.retrieval_workers[random.randint(0     ,						len(self.retrieval_workers					) - 1					)]
													__lowercase      ,				__lowercase      ,				__lowercase						      =					ray.get(
													    random_worker.retrieve.remote(_lowerCamelCase     ,						_lowerCamelCase					)					)
										else:
													__lowercase      ,				__lowercase						      =					self._main_retrieve(_lowerCamelCase     ,						_lowerCamelCase					)
													__lowercase						      =					self.index.get_doc_dicts(_lowerCamelCase					)
										return retrieved_doc_embeds, doc_ids, doc_dicts
							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						**_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										return super(_lowerCamelCase     ,						cls					).get_tokenizers(_lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase					)
							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						**_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					kwargs.pop("config"     ,						_lowerCamelCase					) or RagConfig.from_pretrained(_lowerCamelCase     ,						**_lowerCamelCase					)
										__lowercase						      =					RagTokenizer.from_pretrained(_lowerCamelCase     ,						config=_lowerCamelCase					)
										__lowercase						      =					rag_tokenizer.question_encoder
										__lowercase						      =					rag_tokenizer.generator

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

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





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




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

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







"""simple docstring"""






import os



def     lowerCAmelCase__ (		)	->Tuple:
			__lowercase						      =					0
			__lowercase						      =					0
			with open(os.path.dirname(__magic_name__			) + "/p022_names.txt"			) as file:
						__lowercase						      =					str(file.readlines()[0]			)
						__lowercase						      =					name.replace("\"" ,      ""			).split(","			)

			name.sort()
			for i in range(len(__magic_name__			)			):
						for j in name[i]:
									temp_sum += ord(__magic_name__			) - ord("A"			) + 1
						total_sum += (i + 1) * temp_sum
						__lowercase						      =					0
			return total_sum


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





