


'''simple docstring'''
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,
)
UpperCamelCase__          = logging.getLogger(__name__)





def    a__			(   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						Any						=							git.Repo(search_parent_directories=snake_case__		)
    UpperCAmelCase__     :						int						=							{
        '''repo_id''': str(snake_case__		),
        '''repo_sha''': str(repo.head.object.hexsha		),
        '''repo_branch''': str(repo.active_branch		),
    }

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





def    a__			(   lowerCAmelCase__		)       ->      Tuple:



    if params.n_gpu <= 0:
        UpperCAmelCase__     :						Optional[int]						=							0
        UpperCAmelCase__     :						List[str]						=							-1
        UpperCAmelCase__     :						Union[str, Any]						=							True
        UpperCAmelCase__     :						int						=							False
        return

    assert torch.cuda.is_available()

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

        UpperCAmelCase__     :						Tuple						=							int(os.environ['''WORLD_SIZE''']		)
        UpperCAmelCase__     :						str						=							int(os.environ['''N_GPU_NODE''']		)
        UpperCAmelCase__     :						Any						=							int(os.environ['''RANK''']		)

        # number of nodes / node ID
        UpperCAmelCase__     :						Union[str, Any]						=							params.world_size // params.n_gpu_per_node
        UpperCAmelCase__     :						Any						=							params.global_rank // params.n_gpu_per_node
        UpperCAmelCase__     :						List[Any]						=							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

        UpperCAmelCase__     :						List[Any]						=							1
        UpperCAmelCase__     :						int						=							0
        UpperCAmelCase__     :						Any						=							0
        UpperCAmelCase__     :						Optional[Any]						=							0
        UpperCAmelCase__     :						Tuple						=							1
        UpperCAmelCase__     :						Any						=							1
        UpperCAmelCase__     :						List[Any]						=							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
    UpperCAmelCase__     :						int						=							params.node_id == 0 and params.local_rank == 0
    UpperCAmelCase__     :						List[str]						=							params.n_nodes > 1

    # summary
    UpperCAmelCase__     :						List[Any]						=							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    a__			(   lowerCAmelCase__		)       ->      str:
    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
from collections.abc import Callable







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  float   =		xa
    A			:  float   =		xa
    while True:
        if x_n == x_na or function(snake_case__		) == function(snake_case__		):
            raise ZeroDivisionError('''float division by zero, could not find root'''		)
        A			:  float   =		x_na - (
            function(snake_case__		) / ((function(snake_case__		) - function(snake_case__		)) / (x_na - x_n))
        )
        if abs(x_na - x_na		) < 10**-5:
            return x_na
        A			:  Any   =		x_na
        A			:  Any   =		x_na







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return math.pow(snake_case__						,							3		) - (2 * x) - 5


if __name__ == "__main__":
      print(intersection(f, 3, 3.5))




