





'''simple docstring'''

import logging
import random

import ray

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


lowerCAmelCase__	:					Tuple	     =       logging.getLogger(__name__)


class 			SCREAMING_SNAKE_CASE__	:







					"""simple docstring"""






					def __init__(					self    :	List[str]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Any					   =			False
					def 						lowerCamelCase_					(					self    :	Any						,     UpperCAmelCase_    :	Optional[int]						,     UpperCAmelCase_    :	Tuple						,     UpperCAmelCase_    :	Optional[Any]						,     UpperCAmelCase_    :	Any						):







							"""simple docstring"""
							if not self.initialized:
									__UpperCAmelCase     :				Optional[Any]					   =			RagRetriever(
									    _UpperCAmelCase						,     question_encoder_tokenizer=_UpperCAmelCase						,     generator_tokenizer=_UpperCAmelCase						,     index=_UpperCAmelCase						,     init_retrieval=_UpperCAmelCase						,     )
									__UpperCAmelCase     :				List[str]					   =			True
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							self.retriever.index.init_index()
					def 						lowerCamelCase_					(					self    :	List[Any]						):







							"""simple docstring"""
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							__UpperCAmelCase     :				Tuple					   =			False






					def 						lowerCamelCase_					(					self    :	Union[str, Any]						,     UpperCAmelCase_    :	str						,     UpperCAmelCase_    :	int						):







							"""simple docstring"""
							__UpperCAmelCase   ,       __UpperCAmelCase     :				Dict					   =			self.retriever._main_retrieve(_UpperCAmelCase						,     _UpperCAmelCase						)
							__UpperCAmelCase     :				Optional[int]					   =			self.retriever.index.get_doc_dicts(_UpperCAmelCase						)
							return doc_ids, retrieved_doc_embeds, doc_dicts






class 			SCREAMING_SNAKE_CASE__	(					lowerCamelCase__						):







					"""simple docstring"""






					def __init__(					self    :	int						,     UpperCAmelCase_    :	Optional[Any]						,     UpperCAmelCase_    :	str						,     UpperCAmelCase_    :	str						,     UpperCAmelCase_    :	List[Any]						,     UpperCAmelCase_    :	Any=None						):







							"""simple docstring"""
							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						,     )

							__UpperCAmelCase     :				int					   =			retrieval_workers
							__UpperCAmelCase     :				str					   =			question_encoder_tokenizer
							__UpperCAmelCase     :				List[Any]					   =			generator_tokenizer
							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    :	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 						lowerCamelCase_					(					self    :	Optional[Any]						,     UpperCAmelCase_    :	Optional[Any]						,     UpperCAmelCase_    :	Dict						):







							"""simple docstring"""
							if len(self.retrieval_workers						) > 0:
									# Select a random retrieval actor.
									__UpperCAmelCase     :				str					   =			self.retrieval_workers[random.randint(0						,     len(self.retrieval_workers						) - 1						)]
									__UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase     :				Union[str, Any]					   =			ray.get(
									    random_worker.retrieve.remote(_UpperCAmelCase						,     _UpperCAmelCase						)						)
							else:
									__UpperCAmelCase   ,       __UpperCAmelCase     :				List[str]					   =			self._main_retrieve(_UpperCAmelCase						,     _UpperCAmelCase						)
									__UpperCAmelCase     :				Union[str, Any]					   =			self.index.get_doc_dicts(_UpperCAmelCase						)
							return retrieved_doc_embeds, doc_ids, doc_dicts
					@classmethod
					def 						lowerCamelCase_					(					cls    :	str						,     UpperCAmelCase_    :	Optional[int]						,     UpperCAmelCase_    :	Dict=None						,     **UpperCAmelCase_    :	Optional[int]						):







							"""simple docstring"""
							return super(_UpperCAmelCase						,     cls						).get_tokenizers(_UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase						)
					@classmethod
					def 						lowerCamelCase_					(					cls    :	str						,     UpperCAmelCase_    :	Any						,     UpperCAmelCase_    :	Any						,     UpperCAmelCase_    :	Optional[int]=None						,     **UpperCAmelCase_    :	List[str]						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[Any]					   =			kwargs.pop("config"						,     _UpperCAmelCase						) or RagConfig.from_pretrained(_UpperCAmelCase						,     **_UpperCAmelCase						)
							__UpperCAmelCase     :				List[Any]					   =			RagTokenizer.from_pretrained(_UpperCAmelCase						,     config=_UpperCAmelCase						)
							__UpperCAmelCase     :				Optional[Any]					   =			rag_tokenizer.question_encoder
							__UpperCAmelCase     :				List[Any]					   =			rag_tokenizer.generator

							if indexed_dataset is not None:
									__UpperCAmelCase     :				Dict					   =			"custom"
									__UpperCAmelCase     :				Any					   =			CustomHFIndex(config.retrieval_vector_size						,     _UpperCAmelCase						)
							else:
									__UpperCAmelCase     :				str					   =			cls._build_index(_UpperCAmelCase						)

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






					def 						lowerCamelCase_					(					self    :	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
							__UpperCAmelCase     :				Union[str, Any]					   =			self._build_index(self.config						)

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config						,     self.question_encoder_tokenizer						,     self.generator_tokenizer						,     _UpperCAmelCase						)
							        for worker in self.retrieval_workers
							    ]						)








def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')