
"""simple docstring"""






import logging
import random

import ray

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


_lowercase							 =							logging.getLogger(__name__)

class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       Dict:




													'''simple docstring'''
													__lowercase						      =					False
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




													'''simple docstring'''
													if not self.initialized:
																			__lowercase						      =					RagRetriever(
																			    _lowerCamelCase     ,						question_encoder_tokenizer=_lowerCamelCase     ,						generator_tokenizer=_lowerCamelCase     ,						index=_lowerCamelCase     ,						init_retrieval=_lowerCamelCase     ,						)
																			__lowercase						      =					True
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




													'''simple docstring'''
													self.retriever.index.init_index()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




													'''simple docstring'''
													del self.retriever
													__lowercase						      =					False





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




													'''simple docstring'''
													__lowercase      ,				__lowercase						      =					self.retriever._main_retrieve(_lowerCamelCase     ,						_lowerCamelCase					)
													__lowercase						      =					self.retriever.index.get_doc_dicts(_lowerCamelCase					)
													return doc_ids, retrieved_doc_embeds, doc_dicts





class 				__a    (   __a					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=None					) ->       str:




													'''simple docstring'''
													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						      =					retrieval_workers
													__lowercase						      =					question_encoder_tokenizer
													__lowercase						      =					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      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




													'''simple docstring'''
													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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[int]:




													'''simple docstring'''
													if len(self.retrieval_workers					) > 0:
																			# Select a random retrieval actor.
																			__lowercase						      =					self.retrieval_workers[random.randint(0     ,						len(self.retrieval_workers					) - 1					)]
																			__lowercase      ,				__lowercase      ,				__lowercase						      =					ray.get(
																			    random_worker.retrieve.remote(_lowerCamelCase     ,						_lowerCamelCase					)					)
													else:
																			__lowercase      ,				__lowercase						      =					self._main_retrieve(_lowerCamelCase     ,						_lowerCamelCase					)
																			__lowercase						      =					self.index.get_doc_dicts(_lowerCamelCase					)
													return retrieved_doc_embeds, doc_ids, doc_dicts
							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						**_lowerCamelCase					) ->       List[Any]:




													'''simple docstring'''
													return super(_lowerCamelCase     ,						cls					).get_tokenizers(_lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase					)
							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						**_lowerCamelCase					) ->       Any:




													'''simple docstring'''
													__lowercase						      =					kwargs.pop("config"     ,						_lowerCamelCase					) or RagConfig.from_pretrained(_lowerCamelCase     ,						**_lowerCamelCase					)
													__lowercase						      =					RagTokenizer.from_pretrained(_lowerCamelCase     ,						config=_lowerCamelCase					)
													__lowercase						      =					rag_tokenizer.question_encoder
													__lowercase						      =					rag_tokenizer.generator

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

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





							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




													'''simple docstring'''
													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						      =					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
													    ]					)







"""simple docstring"""






from scipy.constants import g



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->float:
			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''')





