


import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


SCREAMING_SNAKE_CASE__      :			Dict							=		logging.getLogger(__name__)
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								super().__init__(
								    __lowerCAmelCase       ,					question_encoder_tokenizer=__lowerCAmelCase       ,					generator_tokenizer=__lowerCAmelCase       ,					index=__lowerCAmelCase       ,					init_retrieval=__lowerCAmelCase       ,					)
								__magic_name__		:Union[str, Any]        =      None







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								logger.info('''initializing retrieval'''     )

								# initializing a separate process group for retrieval as the default
								# nccl backend doesn't support gather/scatter operations while gloo
								# is too slow to replace nccl for the core gpu communication
								if dist.is_initialized():
												logger.info('''dist initialized'''     )
												# needs to be set manually
												__magic_name__		:str        =      self._infer_socket_ifname()
												# avoid clash with the NCCL port
												__magic_name__		:List[Any]        =      str(distributed_port + 1     )
												__magic_name__		:Tuple        =      dist.new_group(ranks=__lowerCAmelCase       ,					backend='''gloo'''     )

								# initialize retriever only on the main worker
								if not dist.is_initialized() or self._is_main():
												logger.info('''dist not initialized / main'''     )
												self.index.init_index()

								# all processes wait untill the retriever is initialized by the main process
								if dist.is_initialized():
												torch.distributed.barrier(group=self.process_group     )







				def 			A	(						self     ):
								"""simple docstring"""
								return dist.get_rank(group=self.process_group     ) == 0







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=torch.floataa     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      torch.empty(__lowerCAmelCase       ,					dtype=__lowerCAmelCase     )
								dist.scatter(__lowerCAmelCase       ,					src=0       ,					scatter_list=__lowerCAmelCase       ,					group=self.process_group     )
								return target_tensor







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      psutil.net_if_addrs()
								# a hacky way to deal with varying network interface names
								__magic_name__		:List[str]        =      next((addr for addr in addrs if addr.startswith('''e'''     ))       ,					__lowerCAmelCase     )
								return ifname







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								# single GPU training
								if not dist.is_initialized():
												__magic_name__		:int        =      self._main_retrieve(__lowerCAmelCase       ,					__lowerCAmelCase     )
												return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase     )

								# distributed training
								__magic_name__		:Tuple        =      dist.get_world_size(group=self.process_group     )

								# gather logic
								__magic_name__		:List[str]        =      None
								if self._is_main():
												__magic_name__		:int        =      [torch.empty(question_hidden_states.shape       ,					dtype=torch.floataa     ) for _ in range(__lowerCAmelCase     )]
								dist.gather(torch.tensor(__lowerCAmelCase     )       ,					dst=0       ,					gather_list=__lowerCAmelCase       ,					group=self.process_group     )

								# scatter logic
								__magic_name__		:Tuple        =      question_hidden_states.shape[0]
								__magic_name__		:Union[str, Any]        =      []
								__magic_name__		:Tuple        =      []
								if self._is_main():
												assert len(__lowerCAmelCase     ) == world_size
												__magic_name__		:Tuple        =      self._main_retrieve(torch.cat(__lowerCAmelCase     ).numpy()       ,					__lowerCAmelCase     )
												__magic_name__		:str        =      torch.tensor(__lowerCAmelCase     ), torch.tensor(__lowerCAmelCase     )
												__magic_name__		:Optional[int]        =      self._chunk_tensor(__lowerCAmelCase       ,					__lowerCAmelCase     )
												__magic_name__		:str        =      self._chunk_tensor(__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      self._scattered(__lowerCAmelCase       ,					[n_queries, n_docs]       ,					target_type=torch.intaa     )
								__magic_name__		:int        =      self._scattered(__lowerCAmelCase       ,					[n_queries, n_docs, question_hidden_states.shape[1]]     )

								return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase     )














def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												__magic_name__		:Dict        =      mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case )
								else:
												__magic_name__		:Optional[Any]        =      max(
												    mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case ),  mf_knapsack(i - 1,  snake_case,  snake_case,  j - wt[i - 1] ) + val[i - 1],  )
								__magic_name__		:List[Any]        =      val
				return f[i][j]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[0] * (w + 1) for _ in range(n + 1 )]

				for i in range(1,  n + 1 ):
								for w_ in range(1,  w + 1 ):
												if wt[i - 1] <= w_:
																__magic_name__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not (isinstance(snake_case,  (list, tuple) ) and isinstance(snake_case,  (list, tuple) )):
								raise ValueError(
								    '''Both the weights and values vectors must be either lists or tuples''' )

				__magic_name__		:Dict        =      len(snake_case )
				if num_items != len(snake_case ):
								__magic_name__		:str        =      (
								    '''The number of weights must be the same as the number of values.\n'''
								    f'''But got {num_items} weights and {len(snake_case )} values'''
								)
								raise ValueError(snake_case )
				for i in range(snake_case ):
								if not isinstance(wt[i],  snake_case ):
												__magic_name__		:str        =      (
												    '''All weights must be integers but got weight of '''
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case )

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if i > 0 and j > 0:
								if dp[i - 1][j] == dp[i][j]:
												_construct_solution(snake_case,  snake_case,  i - 1,  snake_case,  snake_case )
								else:
												optimal_set.add(snake_case )
												_construct_solution(snake_case,  snake_case,  i - 1,  j - wt[i - 1],  snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		[3, 2, 4, 4]
					SCREAMING_SNAKE_CASE__      :			Any							=		[4, 3, 2, 3]
					SCREAMING_SNAKE_CASE__      :			List[str]							=		4
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		6
					SCREAMING_SNAKE_CASE__      :			str							=		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)






