





"""simple docstring"""





import logging
import random

import ray

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


SCREAMING_SNAKE_CASE__ =       logging.getLogger(__name__)

class 		lowercase :





      def __init__(	self			)		->					str:
            lowerCAmelCase  						=	False





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase			)		->					List[str]:
            if not self.initialized:
                  lowerCAmelCase  						=	RagRetriever(
                      lowercase				,						question_encoder_tokenizer=lowercase				,						generator_tokenizer=lowercase				,						index=lowercase				,						init_retrieval=lowercase				,						)
                  lowerCAmelCase  						=	True





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





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





      def    _snake_case      (	self				,						lowercase				,						lowercase			)		->					Dict:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.retriever._main_retrieve(lowercase				,						lowercase			)
            lowerCAmelCase  						=	self.retriever.index.get_doc_dicts(lowercase			)
            return doc_ids, retrieved_doc_embeds, doc_dicts







class 		lowercase ( _UpperCAmelCase ):





      def __init__(	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase=None			)		->					int:
            if index is not None and index.is_initialized() and len(lowercase			) > 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__(
                lowercase				,						question_encoder_tokenizer=lowercase				,						generator_tokenizer=lowercase				,						index=lowercase				,						init_retrieval=lowercase				,						)

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





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





      @classmethod
      def    _snake_case      (	cls				,						lowercase				,						lowercase=None				,						**lowercase			)		->					Dict:
            return super(lowercase				,						cls			).get_tokenizers(lowercase				,						lowercase				,						**lowercase			)





      @classmethod
      def    _snake_case      (	cls				,						lowercase				,						lowercase				,						lowercase=None				,						**lowercase			)		->					str:
            lowerCAmelCase  						=	kwargs.pop("""config"""				,						lowercase			) or RagConfig.from_pretrained(lowercase				,						**lowercase			)
            lowerCAmelCase  						=	RagTokenizer.from_pretrained(lowercase				,						config=lowercase			)
            lowerCAmelCase  						=	rag_tokenizer.question_encoder
            lowerCAmelCase  						=	rag_tokenizer.generator

            if indexed_dataset is not None:
                  lowerCAmelCase  						=	"""custom"""
                  lowerCAmelCase  						=	CustomHFIndex(config.retrieval_vector_size				,						lowercase			)
            else:
                  lowerCAmelCase  						=	cls._build_index(lowercase			)

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





      def    _snake_case      (	self			)		->					List[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
            lowerCAmelCase  						=	self._build_index(self.config			)

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













"""simple docstring"""





from scipy.constants import g


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						float							,						SCREAMING_SNAKE_CASE					:						float      ):







      '''simple docstring'''




      if mass < 0:
            # handling of negative values of mass
            raise ValueError("""The mass of a body cannot be negative"""      )
      if height < 0:
            # handling of negative values of height
            raise ValueError("""The height above the ground cannot be negative"""      )
      return mass * g * height


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="potential_energy")






