






"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def 			__UpperCamelCase	(		snake_case__					):
	return np.dot(a_   ,   a_					)



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     *,
	    lowerCAmelCase_ = np.inf     ,     lowerCAmelCase_ = "linear"     ,     lowerCAmelCase_ = 0.0     ,     ):
		A_		:						Optional[int]	    =   regularization
		A_		:						Optional[Any]	    =   gamma
		if kernel == "linear":
			A_		:						Tuple	    =   self.__linear
		elif kernel == "rbf":
			if self.gamma == 0:
				raise ValueError("""rbf kernel requires gamma"""							)
			if not isinstance(self.gamma     ,     (float, int)							):
				raise ValueError("""gamma must be float or int"""							)
			if not self.gamma > 0:
				raise ValueError("""gamma must be > 0"""							)
			A_		:						Tuple	    =   self.__rbf
			# in the future, there could be a default value like in sklearn
			# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
			# previously it was 1/(n_features)
		else:
			A_		:						str	    =   f"""Unknown kernel: {kernel}"""
			raise ValueError(__snake_case							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		return np.dot(__snake_case     ,     __snake_case							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   observations
		A_		:						Optional[Any]	    =   classes

		# using Wolfe's Dual to calculate w.
		# Primal problem: minimize 1/2*norm_squared(w)
		#   constraint: yn(w . xn + b) >= 1
		#
		# With l a vector
		# Dual problem: maximize sum_n(ln) -
		#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
		#   constraint: self.C >= ln >= 0
		#           and sum_n(ln*yn) = 0
		# Then we get w using w = sum_n(ln*yn*xn)
		# At the end we can get b ~= mean(yn - w . xn)
		#
		# Since we use kernels, we only need l_star to calculate b
		# and to classify observations

		(A_    )		:						str	    =   np.shape(__snake_case							)

		def to_minimize(lowerCAmelCase_							) -> float:
			A_		:						Dict	    =   0
			(A_    )		:						Dict	    =   np.shape(__snake_case							)
			for i in range(__snake_case							):
				for j in range(__snake_case							):
					s += (
					    candidate[i]
					    * candidate[j]
					    * classes[i]
					    * classes[j]
					    * self.kernel(observations[i]     ,     observations[j]							)
					)
			return 1 / 2 * s - sum(__snake_case							)

		A_		:						Tuple	    =   LinearConstraint(__snake_case     ,     0     ,     0							)
		A_		:						Dict	    =   Bounds(0     ,     self.regularization							)

		A_		:						Union[str, Any]	    =   minimize(
		    __snake_case     ,     np.ones(__snake_case							)     ,     bounds=__snake_case     ,     constraints=[ly_contraint]							).x
		A_		:						List[str]	    =   l_star

		# calculating mean offset of separation plane to points
		A_		:						Union[str, Any]	    =   0
		for i in range(__snake_case							):
			for j in range(__snake_case							):
				s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
				    observations[i]     ,     observations[j]							)
		A_		:						Any	    =   s / n





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   sum(
		    self.optimum[n]
		    * self.classes[n]
		    * self.kernel(self.observations[n]     ,     __snake_case							)
		    for n in range(len(self.classes							)							)							)
		return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()








"""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,
)
_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """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 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, 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_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   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_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						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
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   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	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

