


'''simple docstring'''



import logging
import random

import ray

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


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





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self      )							->	int:
         __lowerCamelCase  :      int														=						False







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         if not self.initialized:
              __lowerCamelCase  :      List[str]														=						RagRetriever(
                  _snake_case     ,	question_encoder_tokenizer=_snake_case     ,	generator_tokenizer=_snake_case     ,	index=_snake_case     ,	init_retrieval=_snake_case     ,	)
              __lowerCamelCase  :      Dict														=						True







    def  lowercase_							(				self      )							->	List[str]:
         self.retriever.index.init_index()







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						self.retriever._main_retrieve(_snake_case     ,	_snake_case      )
         return doc_ids, retrieved_doc_embeds







class   UpperCAmelCase_    (_lowercase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Union[str, Any]:
         if index is not None and index.is_initialized() and len(_snake_case      ) > 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__(
             _snake_case     ,	question_encoder_tokenizer=_snake_case     ,	generator_tokenizer=_snake_case     ,	index=_snake_case     ,	init_retrieval=_snake_case     ,	)
         __lowerCamelCase  :      Any														=						retrieval_workers
         if len(self.retrieval_workers      ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(_snake_case     ,	_snake_case     ,	_snake_case     ,	_snake_case      )
                      for worker in self.retrieval_workers
                  ]      )







    def  lowercase_							(				self      )							->	List[str]:
         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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         if len(self.retrieval_workers      ) > 0:
              # Select a random retrieval actor.
              __lowerCamelCase  :      Tuple														=						self.retrieval_workers[random.randint(0     ,	len(self.retrieval_workers      ) - 1      )]
              __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						ray.get(random_worker.retrieve.remote(_snake_case     ,	_snake_case      )      )
         else:
              __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						self._main_retrieve(_snake_case     ,	_snake_case      )
         return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_snake_case      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         return super(_snake_case     ,	cls      ).get_tokenizers(_snake_case     ,	_snake_case     ,	**_snake_case      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      str														=						kwargs.pop('config'     ,	_snake_case      ) or RagConfig.from_pretrained(_snake_case     ,	**_snake_case      )
         __lowerCamelCase  :      Dict														=						RagTokenizer.from_pretrained(_snake_case     ,	config=_snake_case      )
         __lowerCamelCase  :      int														=						rag_tokenizer.question_encoder
         __lowerCamelCase  :      Optional[Any]														=						rag_tokenizer.generator
         if indexed_dataset is not None:
              __lowerCamelCase  :      Tuple														=						'custom'
              __lowerCamelCase  :      int														=						CustomHFIndex(config.retrieval_vector_size     ,	_snake_case      )
         else:
              __lowerCamelCase  :      List[str]														=						cls._build_index(_snake_case      )
         return cls(
             _snake_case     ,	question_encoder_tokenizer=_snake_case     ,	generator_tokenizer=_snake_case     ,	retrieval_workers=_snake_case     ,	index=_snake_case     ,	)








"""simple docstring"""





from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




