'''simple docstring'''




import logging
import random

import ray

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


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self					)     ->  Optional[int]:
        lowercase_       :							List[Any]  					=       False
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a					)     ->  Optional[Any]:
        if not self.initialized:
             lowercase_       :							Dict  					=       RagRetriever(
                 lowerCAmelCase__							,					question_encoder_tokenizer=lowerCAmelCase__							,					generator_tokenizer=lowerCAmelCase__							,					index=lowerCAmelCase__							,					init_retrieval=lowerCAmelCase__							,					)
             lowercase_       :							Dict  					=       True
   def 		_lowerCamelCase			(self					)     ->  Any:
        self.retriever.index.init_index()
   def 		_lowerCamelCase			(self					)     ->  Dict:
        del self.retriever
        lowercase_       :							Tuple  					=       False





   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Union[str, Any]:
        lowercase_					,lowercase_       :							str  					=       self.retriever._main_retrieve(lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase_       :							Optional[int]  					=       self.retriever.index.get_doc_dicts(lowerCAmelCase__					)
        return doc_ids, retrieved_doc_embeds, doc_dicts



class    UpperCAmelCase__	(							lowercase__      ):






   """simple docstring"""
   def __init__(self							,					_a							,					_a							,					_a							,					_a							,					_a=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__							,					)

        lowercase_       :							List[str]  					=       retrieval_workers
        lowercase_       :							Optional[Any]  					=       question_encoder_tokenizer
        lowercase_       :							List[Any]  					=       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					)     ->  Dict:
        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							,					_a							,					_a					)     ->  List[str]:
        if len(self.retrieval_workers					) > 0:
             # Select a random retrieval actor.
             lowercase_       :							Union[str, Any]  					=       self.retrieval_workers[random.randint(0							,					len(self.retrieval_workers					) - 1					)]
             lowercase_					,lowercase_					,lowercase_       :							List[str]  					=       ray.get(
                 random_worker.retrieve.remote(lowerCAmelCase__							,					lowerCAmelCase__					)					)
        else:
             lowercase_					,lowercase_       :							Any  					=       self._main_retrieve(lowerCAmelCase__							,					lowerCAmelCase__					)
             lowercase_       :							Optional[Any]  					=       self.index.get_doc_dicts(lowerCAmelCase__					)
        return retrieved_doc_embeds, doc_ids, doc_dicts
   @classmethod
   def 		_lowerCamelCase			(cls							,					_a							,					_a=None							,					**_a					)     ->  Union[str, Any]:
        return super(lowerCAmelCase__							,					cls					).get_tokenizers(lowerCAmelCase__							,					lowerCAmelCase__							,					**lowerCAmelCase__					)
   @classmethod
   def 		_lowerCamelCase			(cls							,					_a							,					_a							,					_a=None							,					**_a					)     ->  Dict:
        lowercase_       :							Any  					=       kwargs.pop('config'							,					lowerCAmelCase__					) or RagConfig.from_pretrained(lowerCAmelCase__							,					**lowerCAmelCase__					)
        lowercase_       :							Tuple  					=       RagTokenizer.from_pretrained(lowerCAmelCase__							,					config=lowerCAmelCase__					)
        lowercase_       :							int  					=       rag_tokenizer.question_encoder
        lowercase_       :							Union[str, Any]  					=       rag_tokenizer.generator

        if indexed_dataset is not None:
             lowercase_       :							str  					=       'custom'
             lowercase_       :							Dict  					=       CustomHFIndex(config.retrieval_vector_size							,					lowerCAmelCase__					)
        else:
             lowercase_       :							Union[str, Any]  					=       cls._build_index(lowerCAmelCase__					)

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





   def 		_lowerCamelCase			(self					)     ->  Union[str, 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
        lowercase_       :							Any  					=       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
            ]					)












from __future__ import annotations

import re





def 						A_					(						lowercase_			)				->list[str]:







 """simple docstring"""

 def alphanum_key(lowercase_			):
  return [int(lowercase_			) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'	,  lowercase_			)]

 return sorted(lowercase_	,  key=lowercase_			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




