





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_   :      Tuple			=   logging.getLogger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     git.Repo(search_parent_directories=snake_case							)
		__lowercase											=     {
		    '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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    str:
		np.random.seed(args.seed							)
		torch.manual_seed(args.seed							)
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed							)











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




