
import logging
import random

import ray

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


a__				:      Any    		=     logging.getLogger(__name__)






class     a_       :



       """simple docstring"""






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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[Any]:




           if not self.initialized:
               SCREAMING_SNAKE_CASE				:				Any				      = RagRetriever(
                   _lowerCamelCase				,       question_encoder_tokenizer=_lowerCamelCase				,       generator_tokenizer=_lowerCamelCase				,       index=_lowerCamelCase				,       init_retrieval=_lowerCamelCase				,       )
               SCREAMING_SNAKE_CASE				:				Dict				      = True






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           self.retriever.index.init_index()






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           del self.retriever
           SCREAMING_SNAKE_CASE				:				int				      = False






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.retriever._main_retrieve(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.retriever.index.get_doc_dicts(_lowerCamelCase   )
           return doc_ids, retrieved_doc_embeds, doc_dicts






class     a_       (			lowerCamelCase_							):



       """simple docstring"""






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




           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				:				List[Any]				      = retrieval_workers
           SCREAMING_SNAKE_CASE				:				Tuple				      = question_encoder_tokenizer
           SCREAMING_SNAKE_CASE				:				Tuple				      = 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 	__lowerCAmelCase     (		self   )					->Any:
           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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Any:




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






       @classmethod
       def 	__lowerCAmelCase     (		cls				,       _lowerCamelCase				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->Tuple:
           return super(_lowerCamelCase				,       cls   ).get_tokenizers(_lowerCamelCase				,       _lowerCamelCase				,       **_lowerCamelCase   )






       @classmethod
       def 	__lowerCAmelCase     (		cls				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = kwargs.pop('''config'''				,       _lowerCamelCase   ) or RagConfig.from_pretrained(_lowerCamelCase				,       **_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = RagTokenizer.from_pretrained(_lowerCamelCase				,       config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE				:				Any				      = rag_tokenizer.generator

           if indexed_dataset is not None:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''custom'''
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = CustomHFIndex(config.retrieval_vector_size				,       _lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				Tuple				      = cls._build_index(_lowerCamelCase   )

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






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           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
           SCREAMING_SNAKE_CASE				:				int				      = 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'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




