




import logging
import random

import ray

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


UpperCAmelCase__						:       Any							=    logging.getLogger(__name__)

class      UpperCAmelCase						:






   def __init__(	self   :       Union[str, Any]     )->			Union[str, Any]:
     _lowercase: Any						     =    False






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       str     )->			List[str]:

     if not self.initialized:
       _lowercase: Tuple						     =    RagRetriever(
           __SCREAMING_SNAKE_CASE						,					question_encoder_tokenizer=__SCREAMING_SNAKE_CASE						,					generator_tokenizer=__SCREAMING_SNAKE_CASE						,					index=__SCREAMING_SNAKE_CASE						,					init_retrieval=__SCREAMING_SNAKE_CASE						,					)
       _lowercase: int						     =    True






   def  _a						(	self   :       List[Any]     )->			Optional[int]:
     self.retriever.index.init_index()






   def  _a						(	self   :       Optional[Any]     )->			Dict:
     del self.retriever
     _lowercase: Dict						     =    False






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       Optional[int]     )->			Optional[int]:
     _lowercase     ,					_lowercase: List[Any]						     =    self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE     )
     _lowercase: List[Any]						     =    self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE     )
     return doc_ids, retrieved_doc_embeds, doc_dicts

class      UpperCAmelCase						(			lowerCAmelCase_   ):






   def __init__(	self   :       str						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[int]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Any=None     )->			Tuple:

     if index is not None and index.is_initialized() and len(__SCREAMING_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__(
         __SCREAMING_SNAKE_CASE						,					question_encoder_tokenizer=__SCREAMING_SNAKE_CASE						,					generator_tokenizer=__SCREAMING_SNAKE_CASE						,					index=__SCREAMING_SNAKE_CASE						,					init_retrieval=__SCREAMING_SNAKE_CASE						,					)

     _lowercase: Union[str, Any]						     =    retrieval_workers
     _lowercase: Dict						     =    question_encoder_tokenizer
     _lowercase: List[Any]						     =    generator_tokenizer
     if len(self.retrieval_workers     ) > 0:
       ray.get(
           [
               worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE     )
               for worker in self.retrieval_workers
           ]     )






   def  _a						(	self   :       str     )->			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   :       Optional[int]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       Dict     )->			Optional[int]:

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






   @classmethod
   def  _a						(	cls   :       Any						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       Union[str, Any]=None						,					**UpperCamelCase_   :       Any     )->			str:
     return super(__SCREAMING_SNAKE_CASE						,					cls     ).get_tokenizers(__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					**__SCREAMING_SNAKE_CASE     )






   @classmethod
   def  _a						(	cls   :       List[str]						,					UpperCamelCase_   :       Optional[int]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       str=None						,					**UpperCamelCase_   :       str     )->			str:
     _lowercase: List[str]						     =    kwargs.pop('config'						,					__SCREAMING_SNAKE_CASE     ) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE						,					**__SCREAMING_SNAKE_CASE     )
     _lowercase: str						     =    RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE						,					config=__SCREAMING_SNAKE_CASE     )
     _lowercase: Union[str, Any]						     =    rag_tokenizer.question_encoder
     _lowercase: List[str]						     =    rag_tokenizer.generator

     if indexed_dataset is not None:
       _lowercase: int						     =    'custom'
       _lowercase: Optional[Any]						     =    CustomHFIndex(config.retrieval_vector_size						,					__SCREAMING_SNAKE_CASE     )
     else:
       _lowercase: str						     =    cls._build_index(__SCREAMING_SNAKE_CASE     )

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






   def  _a						(	self   :       Optional[int]     )->			str:
     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: Dict						     =    self._build_index(self.config     )

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
