

'''simple docstring'''

import logging
import random

import ray

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


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.getLogger(__name__)






class    snake_case     :






				"""simple docstring"""



				def __init__(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				False



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							str:

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



				def   a__					(    self       )     ->							Optional[Any]:
											self.retriever.index.init_index()





				def   a__					(    self,     _lowercase,     _lowercase       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.retriever._main_retrieve(lowerCamelCase__,     lowerCamelCase__       )
											return doc_ids, retrieved_doc_embeds






class    snake_case     (      UpperCAmelCase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase=None       )     ->							Optional[Any]:

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



				def   a__					(    self       )     ->							Optional[int]:
											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,     _lowercase,     _lowercase       )     ->							Optional[Any]:

											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_				=				ray.get(random_worker.retrieve.remote(lowerCamelCase__,     lowerCamelCase__       )       )
											else:
																		SCREAMING_SNAKE_CASE_				=				self._main_retrieve(lowerCamelCase__,     lowerCamelCase__       )
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__       )



				@classmethod
				def   a__					(    cls,     _lowercase,     _lowercase=None,     **_lowercase       )     ->							Any:
											return super(lowerCamelCase__,     cls       ).get_tokenizers(lowerCamelCase__,     lowerCamelCase__,     **lowerCamelCase__       )





				@classmethod
				def   a__					(    cls,     _lowercase,     _lowercase,     _lowercase=None,     **_lowercase       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				kwargs.pop('config',     lowerCamelCase__       ) or RagConfig.from_pretrained(lowerCamelCase__,     **lowerCamelCase__       )
											SCREAMING_SNAKE_CASE_				=				RagTokenizer.from_pretrained(lowerCamelCase__,     config=lowerCamelCase__       )
											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,     lowerCamelCase__       )
											else:
																		SCREAMING_SNAKE_CASE_				=				cls._build_index(lowerCamelCase__       )
											return cls(
											    lowerCamelCase__,     question_encoder_tokenizer=lowerCamelCase__,     generator_tokenizer=lowerCamelCase__,     retrieval_workers=lowerCamelCase__,     index=lowerCamelCase__,     )













def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  bool:
   if not all(x.isalpha() for x in string							):
      raise ValueError("""String must only contain alphabetic characters."""							)

   __UpperCamelCase	:			List[str]					=					sorted(string.lower()							)
   return len(__lowerCAmelCase							) == len(set(__lowerCAmelCase							)							)


if __name__ == "__main__":
       UpperCamelCase    							=      input('Enter a string ').strip()

       UpperCamelCase    							=      is_isogram(input_str)
       print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")



