
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
lowercase__		:Union[str, Any]   =    logging.getLogger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						git.Repo(search_parent_directories=lowerCAmelCase__		)
					lowercase							=						{
					    '''repo_id''': str(lowerCAmelCase__		),
					    '''repo_sha''': str(repo.head.object.hexsha		),
					    '''repo_branch''': str(repo.active_branch		),
					}

					with open(os.path.join(lowerCAmelCase__ ,			'''git_log.json'''		) ,			'''w'''		) as f:
										json.dump(lowerCAmelCase__ ,			lowerCAmelCase__ ,			indent=4		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if params.n_gpu <= 0:
										lowercase							=						0
										lowercase							=						-1
										lowercase							=						True
										lowercase							=						False
										return

					assert torch.cuda.is_available()

					logger.info('''Initializing GPUs'''		)
					if params.n_gpu > 1:
										assert params.local_rank != -1

										lowercase							=						int(os.environ['''WORLD_SIZE''']		)
										lowercase							=						int(os.environ['''N_GPU_NODE''']		)
										lowercase							=						int(os.environ['''RANK''']		)

										# number of nodes / node ID
										lowercase							=						params.world_size // params.n_gpu_per_node
										lowercase							=						params.global_rank // params.n_gpu_per_node
										lowercase							=						True

										assert params.n_nodes == int(os.environ['''N_NODES''']		)
										assert params.node_id == int(os.environ['''NODE_RANK''']		)

					# local job (single GPU)
					else:
										assert params.local_rank == -1

										lowercase							=						1
										lowercase							=						0
										lowercase							=						0
										lowercase							=						0
										lowercase							=						1
										lowercase							=						1
										lowercase							=						False

					# sanity checks
					assert params.n_nodes >= 1
					assert 0 <= params.node_id < params.n_nodes
					assert 0 <= params.local_rank <= params.global_rank < params.world_size
					assert params.world_size == params.n_nodes * params.n_gpu_per_node

					# define whether this is the master process / if we are in multi-node distributed mode
					lowercase							=						params.node_id == 0 and params.local_rank == 0
					lowercase							=						params.n_nodes > 1

					# summary
					lowercase							=						f'--- Global rank: {params.global_rank} - '
					logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes		)
					logger.info(PREFIX + '''Node ID        : %i''' % params.node_id		)
					logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank		)
					logger.info(PREFIX + '''World size     : %i''' % params.world_size		)
					logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node		)
					logger.info(PREFIX + '''Master         : %s''' % str(params.is_master		)		)
					logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node		)		)
					logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu		)		)
					logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()		)

					# set GPU device
					torch.cuda.set_device(params.local_rank		)

					# initialize multi-GPU
					if params.multi_gpu:
										logger.info('''Initializing PyTorch distributed'''		)
										torch.distributed.init_process_group(
										    init_method='''env://''' ,			backend='''nccl''' ,			)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					np.random.seed(args.seed		)
					torch.manual_seed(args.seed		)
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed		)








"""simple docstring"""






import math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

							while start <= end:
														if temp[start] is True:
																					in_prime.append(_UpperCamelCase							)
																					for i in range(start * start       ,			end + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Dict      							=					False
														start += 1
							prime += in_prime

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

														for j in range(len(_UpperCamelCase							)							):
																					if temp[j] is True:
																												prime.append(j + low							)

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
