

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 						lowercase_					:



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



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       List[str]    ,      A__   :       Optional[int]    ,      A__   :       Union[str, Any]    ,      A__   :       Union[str, Any]	)					->  Optional[int]:
        if not self.initialized:
          _snake_case		=							RagRetriever(
              A__    ,      question_encoder_tokenizer=A__    ,      generator_tokenizer=A__    ,      index=A__    ,      init_retrieval=A__    ,      )
          _snake_case		=							True



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  str:
        self.retriever.index.init_index()



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  str:
        # delete the old self.retriever object before assigning the new index
        del self.retriever
        _snake_case		=							False







      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       int    ,      A__   :       Dict	)					->  Optional[Any]:
        _snake_case		=							self.retriever._main_retrieve(A__    ,      A__	)
        _snake_case		=							self.retriever.index.get_doc_dicts(A__	)
        return doc_ids, retrieved_doc_embeds, doc_dicts



class 						lowercase_					(						a_  ):



      def __init__(    self   :       Any    ,      A__   :       Optional[Any]    ,      A__   :       List[Any]    ,      A__   :       int    ,      A__   :       Tuple    ,      A__   :       Tuple=None	)					->  Optional[int]:
        if index is not None and index.is_initialized() and len(A__	) > 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__(
            A__    ,      question_encoder_tokenizer=A__    ,      generator_tokenizer=A__    ,      index=A__    ,      init_retrieval=A__    ,      )

        _snake_case		=							retrieval_workers
        _snake_case		=							question_encoder_tokenizer
        _snake_case		=							generator_tokenizer
        if len(self.retrieval_workers	) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(A__    ,      A__    ,      A__    ,      A__	)
                  for worker in self.retrieval_workers
              ]	)



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



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



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

        if indexed_dataset is not None:
          _snake_case		=							'custom'
          _snake_case		=							CustomHFIndex(config.retrieval_vector_size    ,      A__	)
        else:
          _snake_case		=							cls._build_index(A__	)

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







      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Tuple:
        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
        _snake_case		=							self._build_index(self.config	)

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




from collections import namedtuple

__A    	=			namedtuple('''from_to''', '''from_ to''')

__A    	=			{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.000_0098, 10_13_25),
    '''bar''': from_to(0.98_6923, 1.0_1325),
    '''kilopascal''': from_to(0.0098_6923, 101.325),
    '''megapascal''': from_to(9.8_6923, 0.10_1325),
    '''psi''': from_to(0.06_8046, 14.6959),
    '''inHg''': from_to(0.033_4211, 29.9213),
    '''torr''': from_to(0.0013_1579, 7_60),
}



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			float:



  """simple docstring"""






  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

