




'''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      __UpperCAmelCase							:



					'''simple docstring'''





					def __init__(  self    :	List[Any])						->       Dict:
								A_       		=   False



					def 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	Optional[Any]			,					_lowercase    :	Optional[Any]			,					_lowercase    :	Any			,					_lowercase    :	List[str])						->       Tuple:
								if not self.initialized:
											A_       		=   RagRetriever(
											    __A			,					question_encoder_tokenizer=__A			,					generator_tokenizer=__A			,					index=__A			,					init_retrieval=__A			,					)
											A_       		=   True



					def 						__snake_case	(  self    :	Dict)						->       Dict:
								self.retriever.index.init_index()



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



					def 						__snake_case	(  self    :	List[str]			,					_lowercase    :	Any			,					_lowercase    :	List[str])						->       str:
								A_		,				A_       		=   self.retriever._main_retrieve(__A			,					__A)
								A_       		=   self.retriever.index.get_doc_dicts(__A)
								return doc_ids, retrieved_doc_embeds, doc_dicts





class      __UpperCAmelCase							(    _SCREAMING_SNAKE_CASE							):



					'''simple docstring'''





					def __init__(  self    :	Dict			,					_lowercase    :	List[Any]			,					_lowercase    :	List[str]			,					_lowercase    :	List[str]			,					_lowercase    :	List[Any]			,					_lowercase    :	Tuple=None)						->       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			,					)

								A_       		=   retrieval_workers
								A_       		=   question_encoder_tokenizer
								A_       		=   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 						__snake_case	(  self    :	List[str])						->       Union[str, Any]:
								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    :	Any			,					_lowercase    :	Optional[Any]			,					_lowercase    :	Optional[int])						->       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_		,				A_       		=   ray.get(
											    random_worker.retrieve.remote(__A			,					__A))
								else:
											A_		,				A_       		=   self._main_retrieve(__A			,					__A)
											A_       		=   self.index.get_doc_dicts(__A)
								return retrieved_doc_embeds, doc_ids, doc_dicts



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



					@classmethod
					def 						__snake_case	(  cls    :	int			,					_lowercase    :	str			,					_lowercase    :	List[str]			,					_lowercase    :	Dict=None			,					**_lowercase    :	Any)						->       Optional[Any]:
								A_       		=   kwargs.pop('config'			,					__A) or RagConfig.from_pretrained(__A			,					**__A)
								A_       		=   RagTokenizer.from_pretrained(__A			,					config=__A)
								A_       		=   rag_tokenizer.question_encoder
								A_       		=   rag_tokenizer.generator

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

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



					def 						__snake_case	(  self    :	str)						->       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
								A_       		=   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
								    ])










'''simple docstring'''
from unittest.mock import patch

import datasets
from datasets import Dataset





def  lowerCamelCase(       )		->		Any:
			A_       		=   Dataset.from_dict({'col_1': [3, 2, 0, 1]}    )

			with patch('tqdm.auto.tqdm'    ) as mock_tqdm:
						datasets.disable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_not_called()

						mock_tqdm.reset_mock()

						datasets.enable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_called()




