



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 			_snake_case     :




				def __init__(  self   :  Optional[Any]  ):
							lowercase__   					=			False




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[Any],   __lowercase   :  str  ):
							if not self.initialized:
										lowercase__   					=			RagRetriever(
										    __lowercase,   question_encoder_tokenizer=__lowercase,   generator_tokenizer=__lowercase,   index=__lowercase,   init_retrieval=__lowercase,   )
										lowercase__   					=			True




				def 							A__ (  self   :  Optional[Any]  ):
							self.retriever.index.init_index()




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Dict,   __lowercase   :  Optional[Any]  ):
							lowercase__					,    lowercase__   					=			self.retriever._main_retrieve(__lowercase,   __lowercase  )
							return doc_ids, retrieved_doc_embeds
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Dict,   __lowercase   :  Any,   __lowercase   :  str,   __lowercase   :  str,   __lowercase   :  Tuple,   __lowercase   :  List[str]=None  ):
							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,   )
							lowercase__   					=			retrieval_workers
							if len(self.retrieval_workers  ) > 0:
										ray.get(
										    [
										        worker.create_rag_retriever.remote(__lowercase,   __lowercase,   __lowercase,   __lowercase  )
										        for worker in self.retrieval_workers
										    ]  )




				def 							A__ (  self   :  Dict  ):
							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 							A__ (  self   :  Optional[int],   __lowercase   :  List[str],   __lowercase   :  List[Any]  ):
							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__   					=			ray.get(random_worker.retrieve.remote(__lowercase,   __lowercase  )  )
							else:
										lowercase__					,    lowercase__   					=			self._main_retrieve(__lowercase,   __lowercase  )
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowercase  )




				@classmethod
				def 							A__ (  cls   :  str,   __lowercase   :  Any,   __lowercase   :  List[str]=None,   **__lowercase   :  Union[str, Any]  ):
							return super(__lowercase,   cls  ).get_tokenizers(__lowercase,   __lowercase,   **__lowercase  )




				@classmethod
				def 							A__ (  cls   :  int,   __lowercase   :  List[Any],   __lowercase   :  str,   __lowercase   :  int=None,   **__lowercase   :  Optional[int]  ):
							lowercase__   					=			kwargs.pop("config",   __lowercase  ) or RagConfig.from_pretrained(__lowercase,   **__lowercase  )
							lowercase__   					=			RagTokenizer.from_pretrained(__lowercase,   config=__lowercase  )
							lowercase__   					=			rag_tokenizer.question_encoder
							lowercase__   					=			rag_tokenizer.generator
							if indexed_dataset is not None:
										lowercase__   					=			"custom"
										lowercase__   					=			CustomHFIndex(config.retrieval_vector_size,   __lowercase  )
							else:
										lowercase__   					=			cls._build_index(__lowercase  )
							return cls(
							    __lowercase,   question_encoder_tokenizer=__lowercase,   generator_tokenizer=__lowercase,   retrieval_workers=__lowercase,   index=__lowercase,   )









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




