"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
						import tensorflow as tf


lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)
@dataclass
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = [
					    "no_inference",
					    "no_cuda",
					    "no_tpu",
					    "no_speed",
					    "no_memory",
					    "no_env_print",
					    "no_multi_process",
					]







					def __init__(				self   :	Optional[int]    ,**lowerCamelCase__   :	Tuple			):
									for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																	UpperCAmelCase__             =							deprecated_arg[3:]
																	UpperCAmelCase__             =							not kwargs.pop(lowerCamelCase__			)
																	logger.warning(
																	    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
																	    f''' {positive_arg}={kwargs[positive_arg]}'''			)
									UpperCAmelCase__             =							kwargs.pop('tpu_name'    ,self.tpu_name			)
									UpperCAmelCase__             =							kwargs.pop('device_idx'    ,self.device_idx			)
									UpperCAmelCase__             =							kwargs.pop('eager_mode'    ,self.eager_mode			)
									UpperCAmelCase__             =							kwargs.pop('use_xla'    ,self.use_xla			)
									super().__init__(**lowerCamelCase__			)

					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Name of TPU"}      ,    )
					snake_case__				    = field(
					    default=0      ,    metadata={"help": "CPU / GPU device index. Defaults to 0."}      ,    )
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "Benchmark models in eager model."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
					    }      ,    )







					@cached_property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									requires_backends(self    ,['tf']			)
									UpperCAmelCase__             =							None
									if self.tpu:
													try:
																	if self.tpu_name:
																					UpperCAmelCase__             =							tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
																	else:
																					UpperCAmelCase__             =							tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																	UpperCAmelCase__             =							None
									return tpu







					@cached_property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									requires_backends(self    ,['tf']			)
									if self.is_tpu:
													tf.config.experimental_connect_to_cluster(self._setup_tpu			)
													tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

													UpperCAmelCase__             =							tf.distribute.TPUStrategy(self._setup_tpu			)
									else:
													# currently no multi gpu is allowed
													if self.is_gpu:
																	# TODO: Currently only single GPU is supported
																	tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,'GPU'			)
																	UpperCAmelCase__             =							tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''			)
													else:
																	tf.config.set_visible_devices([]    ,'GPU'			)  # disable GPU
																	UpperCAmelCase__             =							tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''			)

									return strategy







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									requires_backends(self    ,['tf']			)
									return self._setup_tpu is not None







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									requires_backends(self    ,['tf']			)
									return self._setup_strategy







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									requires_backends(self    ,['tf']			)
									return tf.config.list_physical_devices('GPU'			)







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									requires_backends(self    ,['tf']			)
									if self.cuda:
													return len(self.gpu_list			)
									return 0







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									return self.n_gpu > 0




"""simple docstring"""






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



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							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'			)
													UpperCAmelCase__             =							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:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							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

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

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

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

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







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



