


'''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,
)
A__:    int					    =       logging.getLogger(__name__)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			Dict:
       _a    : Any     					=git.Repo(search_parent_directories=_lowerCamelCase  )
       _a    : Dict     					={
           "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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Dict  )  ->			List[str]:
       if params.n_gpu <= 0:
              _a    : str     					=0
              _a    : int     					=-1
              _a    : Tuple     					=True
              _a    : List[Any]     					=False
              return

       assert torch.cuda.is_available()

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

              _a    : str     					=int(os.environ["""WORLD_SIZE"""]  )
              _a    : Any     					=int(os.environ["""N_GPU_NODE"""]  )
              _a    : int     					=int(os.environ["""RANK"""]  )

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

              _a    : Tuple     					=1
              _a    : Any     					=0
              _a    : Optional[Any]     					=0
              _a    : str     					=0
              _a    : Union[str, Any]     					=1
              _a    : int     					=1
              _a    : int     					=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
       _a    : Tuple     					=params.node_id == 0 and params.local_rank == 0
       _a    : Dict     					=params.n_nodes > 1

       # summary
       _a    : Optional[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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any  )  ->			List[str]:
       np.random.seed(args.seed  )
       torch.manual_seed(args.seed  )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed  )



UpperCamelCase  							=						[0, 2, 4, 6, 8]
UpperCamelCase  							=						[1, 3, 5, 7, 9]

def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       list[int]		,	_lowerCamelCase							:       int):
    if remaining_length == 0:
        if digits[0] == 0 or digits[-1] == 0:
            return 0

        for i in range(length // 2 - 1		,	-1		,	-1):
            remainder += digits[i] + digits[length - i - 1]

            if remainder % 2 == 0:
                return 0

            remainder //= 10

        return 1

    if remaining_length == 1:
        if remainder % 2 == 0:
            return 0

        lowercase__ :   str    					=				0
        for digit in range(10):
            lowercase__ :   str    					=				digit
            result += reversible_numbers(
                0		,	(remainder + 2 * digit) // 10		,	_lowerCamelCase		,	_lowerCamelCase)
        return result

    lowercase__ :   Dict    					=				0
    for digita in range(10):
        lowercase__ :   int    					=				digita

        if (remainder + digita) % 2 == 0:
            lowercase__ :   Optional[Any]    					=				ODD_DIGITS
        else:
            lowercase__ :   str    					=				EVEN_DIGITS

        for digita in other_parity_digits:
            lowercase__ :   List[str]    					=				digita
            result += reversible_numbers(
                remaining_length - 2		,	(remainder + digita + digita) // 10		,	_lowerCamelCase		,	_lowerCamelCase		,	)
    return result

def 			lowercase_				(			_lowerCamelCase							:       int = 9):
    lowercase__ :   Tuple    					=				0
    for length in range(1		,	max_power + 1):
        result += reversible_numbers(_lowerCamelCase		,	0		,	[0] * length		,	_lowerCamelCase)
    return result


if __name__ == "__main__":
      print(f"{solution() = }")



