
import logging
import random

import ray

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


_lowerCamelCase :       Optional[int]     					=    logging.getLogger(__name__)




class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	def __init__(						self			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    False



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Optional[int]    ,						lowercase			:					Union[str, Any]    ,						lowercase			:					Union[str, Any]			):


				'''simple docstring'''


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



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


				'''simple docstring'''


				self.retriever.index.init_index()



	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				del self.retriever
				_snake_case						=    False





	def 				A							(						self			:					Optional[int]    ,						lowercase			:					int    ,						lowercase			:					Dict			):


				'''simple docstring'''


				_snake_case						=    self.retriever._main_retrieve(lowercase    ,						lowercase			)
				_snake_case						=    self.retriever.index.get_doc_dicts(lowercase			)
				return doc_ids, retrieved_doc_embeds, doc_dicts




class      SCREAMING_SNAKE_CASE__  (			a_      ):





	'''simple docstring'''



	def __init__(						self			:					Any    ,						lowercase			:					Optional[Any]    ,						lowercase			:					List[Any]    ,						lowercase			:					int    ,						lowercase			:					Tuple    ,						lowercase			:					Tuple=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    ,						)

				_snake_case						=    retrieval_workers
				_snake_case						=    question_encoder_tokenizer
				_snake_case						=    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 				A							(						self			:					List[Any]			):


				'''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 				A							(						self			:					Optional[Any]    ,						lowercase			:					Dict    ,						lowercase			:					str			):


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	@classmethod
	def 				A							(						cls			:					List[str]    ,						lowercase			:					Optional[Any]    ,						lowercase			:					Optional[Any]    ,						lowercase			:					List[Any]=None    ,						**lowercase			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    kwargs.pop('config'    ,						lowercase			) or RagConfig.from_pretrained(lowercase    ,						**lowercase			)
				_snake_case						=    RagTokenizer.from_pretrained(lowercase    ,						config=lowercase			)
				_snake_case						=    rag_tokenizer.question_encoder
				_snake_case						=    rag_tokenizer.generator

				if indexed_dataset is not None:
							_snake_case						=    'custom'
							_snake_case						=    CustomHFIndex(config.retrieval_vector_size    ,						lowercase			)
				else:
							_snake_case						=    cls._build_index(lowercase			)

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





	def 				A							(						self			:					Optional[Any]			):


				'''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
				_snake_case						=    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
				    ]			)

import random
import sys

_lowerCamelCase :       Tuple     					=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def  a_	( )				->						None:
			_snake_case						=    input('Enter message: '				)
			_snake_case						=    'LFWOAYUISVKMNXPBDCRJTQEGHZ'
			_snake_case						=    input('Encrypt/Decrypt [e/d]: '				)

			check_valid_key(__lowercase				)

			if resp.lower().startswith('e'				):
						_snake_case						=    'encrypt'
						_snake_case						=    encrypt_message(__lowercase ,      __lowercase				)
			elif resp.lower().startswith('d'				):
						_snake_case						=    'decrypt'
						_snake_case						=    decrypt_message(__lowercase ,      __lowercase				)

			print(f'''\n{mode.title()}ion: \n{translated}'''				)






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    list(__lowercase				)
			_snake_case						=    list(__lowercase				)
			key_list.sort()
			letters_list.sort()

			if key_list != letters_list:
						sys.exit('Error in the key or symbol set.'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'encrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'decrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			_snake_case						=    ''
			_snake_case						=    LETTERS
			_snake_case						=    key

			if mode == "decrypt":
						_snake_case			,      _snake_case						=    chars_b, chars_a

			for symbol in message:
						if symbol.upper() in chars_a:
									_snake_case						=    chars_a.find(symbol.upper()				)
									if symbol.isupper():
												translated += chars_b[sym_index].upper()
									else:
												translated += chars_b[sym_index].lower()
						else:
									translated += symbol

			return translated






def  a_	( )				->						str:
			_snake_case						=    list(__lowercase				)
			random.shuffle(__lowercase				)
			return "".join(__lowercase				)


if __name__ == "__main__":
			main()