





'''simple docstring'''




import logging
import random

import ray

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


a__					:			Optional[int]          =     logging.getLogger(__name__)






class 				__snake_case     :





	def __init__(		self		)      ->		Dict:
		snake_case__	       =   False





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		if not self.initialized:
			snake_case__	       =   RagRetriever(
			    UpperCamelCase_      ,       question_encoder_tokenizer=UpperCamelCase_      ,       generator_tokenizer=UpperCamelCase_      ,       index=UpperCamelCase_      ,       init_retrieval=UpperCamelCase_      ,       )
			snake_case__	       =   True





	def   _snake_case   (		self		)      ->		Optional[int]:
		self.retriever.index.init_index()





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Union[str, Any]:
		snake_case__       ,  snake_case__	       =   self.retriever._main_retrieve(UpperCamelCase_      ,       UpperCamelCase_		)
		return doc_ids, retrieved_doc_embeds






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Any:
		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_      ,       )
		snake_case__	       =   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   _snake_case   (		self		)      ->		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      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[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__       ,  snake_case__	       =   ray.get(random_worker.retrieve.remote(UpperCamelCase_      ,       UpperCamelCase_		)		)
		else:
			snake_case__       ,  snake_case__	       =   self._main_retrieve(UpperCamelCase_      ,       UpperCamelCase_		)
		return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_		)





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





	@classmethod
	def   _snake_case   (		cls      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None      ,       **UpperCamelCase_		)      ->		Union[str, Any]:
		snake_case__	       =   kwargs.pop('config'      ,       UpperCamelCase_		) or RagConfig.from_pretrained(UpperCamelCase_      ,       **UpperCamelCase_		)
		snake_case__	       =   RagTokenizer.from_pretrained(UpperCamelCase_      ,       config=UpperCamelCase_		)
		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      ,       UpperCamelCase_		)
		else:
			snake_case__	       =   cls._build_index(UpperCamelCase_		)
		return cls(
		    UpperCamelCase_      ,       question_encoder_tokenizer=UpperCamelCase_      ,       generator_tokenizer=UpperCamelCase_      ,       retrieval_workers=UpperCamelCase_      ,       index=UpperCamelCase_      ,       )










'''simple docstring'''




import enum
import shutil
import sys


a__      ,       a__					:			Any          =     shutil.get_terminal_size()

a__					:			Optional[int]          =     {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}






class 				__snake_case     (					enum.Enum							):
	__lowerCAmelCase       						=  0
	__lowerCAmelCase       						=  1





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->Optional[Any]:
	sys.stdout.write(str(UpperCAmelCase_							) + end							)
	sys.stdout.flush()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->List[str]:
	forceWrite(f'''\u001b[{color}m{content}\u001b[0m'''					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->Optional[Any]:
	forceWrite('\r'							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	forceWrite(f'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''							)




def 						__lowerCamelCase			(	)   ->str:
	forceWrite(' ' * TERMINAL_WIDTH							)
	reset_cursor()







def 						__lowerCamelCase			(	)   ->Tuple:
	reset_cursor()
	forceWrite('-' * TERMINAL_WIDTH							)



