
"""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,
)
_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       git.Repo(search_parent_directories=UpperCamelCase__ )
							__snake_case     			=       {
							    "repo_id": str(UpperCamelCase__ ),
							    "repo_sha": str(repo.head.object.hexsha ),
							    "repo_branch": str(repo.active_branch ),
							}

							with open(os.path.join(UpperCamelCase__							,    "git_log.json" )							,    "w" ) as f:
														json.dump(UpperCamelCase__							,    UpperCamelCase__							,    indent=4 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							if params.n_gpu <= 0:
														__snake_case     			=       0
														__snake_case     			=       -1
														__snake_case     			=       True
														__snake_case     			=       False
														return

							assert torch.cuda.is_available()

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

														__snake_case     			=       int(os.environ["WORLD_SIZE"] )
														__snake_case     			=       int(os.environ["N_GPU_NODE"] )
														__snake_case     			=       int(os.environ["RANK"] )

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

														__snake_case     			=       1
														__snake_case     			=       0
														__snake_case     			=       0
														__snake_case     			=       0
														__snake_case     			=       1
														__snake_case     			=       1
														__snake_case     			=       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
							__snake_case     			=       params.node_id == 0 and params.local_rank == 0
							__snake_case     			=       params.n_nodes > 1

							# summary
							__snake_case     			=       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   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""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"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
