
"""simple docstring"""







import logging
import random

import ray

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


lowerCamelCase__ :    Any        =		logging.getLogger(__name__)




class 			A__	:

		def __init__(				self			):
			__lowerCAmelCase      :						int						      =				False

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):


			if not self.initialized:
				__lowerCAmelCase      :						Any						      =				RagRetriever(
				    __UpperCAmelCase	,		question_encoder_tokenizer=__UpperCAmelCase	,		generator_tokenizer=__UpperCAmelCase	,		index=__UpperCAmelCase	,		init_retrieval=__UpperCAmelCase	,		)
				__lowerCAmelCase      :						Optional[int]						      =				True

		def 							__lowerCamelCase							(				self			):
			self.retriever.index.init_index()







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				self.retriever._main_retrieve(__UpperCAmelCase	,		__UpperCAmelCase			)
			return doc_ids, retrieved_doc_embeds




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):


			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	,		)
			__lowerCAmelCase      :						Dict						      =				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 							__lowerCamelCase							(				self			):
			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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):


			if len(self.retrieval_workers			) > 0:
				# Select a random retrieval actor.
				__lowerCAmelCase      :						Union[str, Any]						      =				self.retrieval_workers[random.randint(0	,		len(self.retrieval_workers			) - 1			)]
				__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				ray.get(random_worker.retrieve.remote(__UpperCAmelCase	,		__UpperCAmelCase			)			)
			else:
				__lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				self._main_retrieve(__UpperCAmelCase	,		__UpperCAmelCase			)
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase			)

		@classmethod
		def 							__lowerCamelCase							(				cls	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			return super(__UpperCAmelCase	,		cls			).get_tokenizers(__UpperCAmelCase	,		__UpperCAmelCase	,		**__UpperCAmelCase			)







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

"""simple docstring"""







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels