


import logging
import random

import ray

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


a : str		=				logging.getLogger(__name__)
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   False






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								if not self.initialized:
											__lowercase 					=   RagRetriever(
											    snake_case_ ,       question_encoder_tokenizer=snake_case_ ,       generator_tokenizer=snake_case_ ,       index=snake_case_ ,       init_retrieval=snake_case_ ,       )
											__lowercase 					=   True






					def A      (				self						)   ->			str:







								'''simple docstring'''

								self.retriever.index.init_index()






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.retriever._main_retrieve(snake_case_ ,       snake_case_						)
								return doc_ids, retrieved_doc_embeds




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								if index is not None and index.is_initialized() and len(snake_case_						) > 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__(
								    snake_case_ ,       question_encoder_tokenizer=snake_case_ ,       generator_tokenizer=snake_case_ ,       index=snake_case_ ,       init_retrieval=snake_case_ ,       )
								__lowercase 					=   retrieval_workers
								if len(self.retrieval_workers						) > 0:
											ray.get(
											    [
											        worker.create_rag_retriever.remote(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)
											        for worker in self.retrieval_workers
											    ]						)






					def A      (				self						)   ->			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 A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								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(snake_case_ ,       snake_case_						)						)
								else:
											__lowercase					, __lowercase 					=   self._main_retrieve(snake_case_ ,       snake_case_						)
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_=None ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								return super(snake_case_ ,       cls						).get_tokenizers(snake_case_ ,       snake_case_ ,       **snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_ ,       snake_case_=None ,       **snake_case_						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   kwargs.pop('''config''' ,       snake_case_						) or RagConfig.from_pretrained(snake_case_ ,       **snake_case_						)
								__lowercase 					=   RagTokenizer.from_pretrained(snake_case_ ,       config=snake_case_						)
								__lowercase 					=   rag_tokenizer.question_encoder
								__lowercase 					=   rag_tokenizer.generator
								if indexed_dataset is not None:
											__lowercase 					=   '''custom'''
											__lowercase 					=   CustomHFIndex(config.retrieval_vector_size ,       snake_case_						)
								else:
											__lowercase 					=   cls._build_index(snake_case_						)
								return cls(
								    snake_case_ ,       question_encoder_tokenizer=snake_case_ ,       generator_tokenizer=snake_case_ ,       retrieval_workers=snake_case_ ,       index=snake_case_ ,       )






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   len(_UpperCamelCase  )
			__lowercase 					=   len(matrix[0]  )
			__lowercase 					=   min(_UpperCamelCase       ,			_UpperCamelCase  )

			for row in range(_UpperCamelCase  ):
						# Check if diagonal element is not zero
						if matrix[row][row] != 0:
									# Eliminate all the elements below the diagonal
									for col in range(row + 1       ,			_UpperCamelCase  ):
												__lowercase 					=   matrix[col][row] / matrix[row][row]
												for i in range(_UpperCamelCase       ,			_UpperCamelCase  ):
															matrix[col][i] -= multiplier * matrix[row][i]
						else:
									# Find a non-zero diagonal element to swap rows
									__lowercase 					=   True
									for i in range(row + 1       ,			_UpperCamelCase  ):
												if matrix[i][row] != 0:
															__lowercase					, __lowercase 					=   matrix[i], matrix[row]
															__lowercase 					=   False
															break
									if reduce:
												rank -= 1
												for i in range(_UpperCamelCase  ):
															__lowercase 					=   matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
									row -= 1

			return rank


if __name__ == "__main__":
	import doctest

	doctest.testmod()


