





'''simple docstring'''




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


a__					:			int          =     logging.getLogger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		List[str]:
		super().__init__(
		    UpperCamelCase_      ,       question_encoder_tokenizer=UpperCamelCase_      ,       generator_tokenizer=UpperCamelCase_      ,       index=UpperCamelCase_      ,       init_retrieval=UpperCamelCase_      ,       )
		snake_case__	       =   None





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[int]:
		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
			snake_case__	       =   self._infer_socket_ifname()
			# avoid clash with the NCCL port
			snake_case__	       =   str(distributed_port + 1		)
			snake_case__	       =   dist.new_group(ranks=UpperCamelCase_      ,       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   _snake_case   (		self		)      ->		Optional[Any]:
		return dist.get_rank(group=self.process_group		) == 0





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=torch.floataa		)      ->		List[str]:
		snake_case__	       =   torch.empty(UpperCamelCase_      ,       dtype=UpperCamelCase_		)
		dist.scatter(UpperCamelCase_      ,       src=0      ,       scatter_list=UpperCamelCase_      ,       group=self.process_group		)
		return target_tensor





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   psutil.net_if_addrs()
		# a hacky way to deal with varying network interface names
		snake_case__	       =   next((addr for addr in addrs if addr.startswith('e'		))      ,       UpperCamelCase_		)
		return ifname





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple[np.ndarray, List[dict]]:

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

		# distributed training
		snake_case__	       =   dist.get_world_size(group=self.process_group		)

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

		# scatter logic
		snake_case__	       =   question_hidden_states.shape[0]
		snake_case__	       =   []
		snake_case__	       =   []
		if self._is_main():
			assert len(UpperCamelCase_		) == world_size
			snake_case__       ,  snake_case__	       =   self._main_retrieve(torch.cat(UpperCamelCase_		).numpy()      ,       UpperCamelCase_		)
			snake_case__       ,  snake_case__	       =   torch.tensor(UpperCamelCase_		), torch.tensor(UpperCamelCase_		)
			snake_case__	       =   self._chunk_tensor(UpperCamelCase_      ,       UpperCamelCase_		)
			snake_case__	       =   self._chunk_tensor(UpperCamelCase_      ,       UpperCamelCase_		)
		snake_case__	       =   self._scattered(UpperCamelCase_      ,       [n_queries, n_docs]      ,       target_type=torch.intaa		)
		snake_case__	       =   self._scattered(UpperCamelCase_      ,       [n_queries, n_docs, question_hidden_states.shape[1]]		)

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










'''simple docstring'''




from __future__ import annotations

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

	if depth < 0:
		raise ValueError('Depth cannot be less than 0'							)

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



