
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_					:						Tuple				= logging.getLogger(__name__)
def 							A_     (__a				):

					'''simple docstring'''





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

					with open(os.path.join(__a				,   "git_log.json"				)				,   "w"				) as f:
										json.dump(__a				,   __a				,   indent=4				)
def 							A_     (__a				):

					'''simple docstring'''





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

					assert torch.cuda.is_available()

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

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

										# number of nodes / node ID
										A_				 =						params.world_size // params.n_gpu_per_node
										A_				 =						params.global_rank // params.n_gpu_per_node
										A_				 =						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_				 =						1
										A_				 =						0
										A_				 =						0
										A_				 =						0
										A_				 =						1
										A_				 =						1
										A_				 =						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_				 =						params.node_id == 0 and params.local_rank == 0
					A_				 =						params.n_nodes > 1

					# summary
					A_				 =						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_     (__a				):

					'''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"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__a				,   __a				,   __a				)

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




