


def    __lowerCamelCase   (      A__						: str					)     ->    int:
   lowerCamelCase_						:   Optional[int]		= 0

   # if input_string is "aba" than new_input_string become "a|b|a"
   lowerCamelCase_						:   Optional[int]		= """"""
   lowerCamelCase_						:   Dict		= """"""

   # append each character + "|" in new_string for range(0, length-1)
   for i in input_string[: len(__snake_case					) - 1]:
      new_input_string += i + "|"
   # append last character
   new_input_string += input_string[-1]

   # we will store the starting and ending of previous furthest ending palindromic
   # substring
   lowerCamelCase_,	lowerCamelCase_						:   str		= 0, 0

   # length[i] shows the length of palindromic substring with center i
   lowerCamelCase_						:   List[str]		= [1 for i in range(len(__snake_case					)					)]

   # for each character in new_string find corresponding palindromic string
   lowerCamelCase_						:   List[str]		= 0
   for j in range(len(__snake_case					)					):
      lowerCamelCase_						:   Optional[Any]		= 1 if j > r else min(length[l + r - j] // 2						,						r - j + 1					)
      while (
          j - k >= 0
          and j + k < len(__snake_case					)
          and new_input_string[k + j] == new_input_string[j - k]
      ):
         k += 1

      lowerCamelCase_						:   List[Any]		= 2 * k - 1

      # does this string is ending after the previously explored end (that is r) ?
      # if yes the update the new r to the last index of this
      if j + k - 1 > r:
         lowerCamelCase_						:   Optional[int]		= j - k + 1  # noqa: E741
         lowerCamelCase_						:   Dict		= j + k - 1

      # update max_length and start position
      if max_length < length[j]:
         lowerCamelCase_						:   str		= length[j]
         lowerCamelCase_						:   Optional[Any]		= j

    # create that string
   lowerCamelCase_						:   int		= new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
   for i in s:
      if i != "|":
         output_string += i

   return output_string


if __name__ == "__main__":
      import doctest

      doctest.testmod()






'''simple docstring'''






import logging
import random

import ray

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


_UpperCAmelCase    :     int         =       logging.getLogger(__name__)







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple     )  ->			int:
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any]     )  ->			Any:
        if not self.initialized:
             _A    		=  RagRetriever(
                 UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
             _A    		=  True
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        self.retriever.index.init_index()

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			Optional[Any]:
        _A		,       _A    		=  self.retriever._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return doc_ids, retrieved_doc_embeds







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int=None     )  ->			Optional[int]:
        if index is not None and index.is_initialized() and len(UpperCamelCase__     ) > 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__(
            UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
        _A    		=  retrieval_workers
        if len(self.retrieval_workers     ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                     for worker in self.retrieval_workers
                 ]     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			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 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple     )  ->			Dict:
        if len(self.retrieval_workers     ) > 0:
             # Select a random retrieval actor.
             _A    		=  self.retrieval_workers[random.randint(0, len(self.retrieval_workers     ) - 1     )]
             _A		,       _A    		=  ray.get(random_worker.retrieve.remote(UpperCamelCase__, UpperCamelCase__     )     )
        else:
             _A		,       _A    		=  self._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase__     )
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			Optional[int]     )  ->			Optional[Any]:
        return super(UpperCamelCase__, cls     ).get_tokenizers(UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     )

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



