




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
						import torch

if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE:     Any									= logging.get_logger(__name__)



@dataclass
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =[
						    "no_inference",
						    "no_cuda",
						    "no_tpu",
						    "no_speed",
						    "no_memory",
						    "no_env_print",
						    "no_multi_process",
						]



						def __init__(			self	:   Any					,							**snake_case__	:   List[Any]  ):

								"""simple docstring"""

								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												SCREAMING_SNAKE_CASE_					      =							deprecated_arg[3:]
												setattr(self					,							snake_case__					,							not kwargs.pop(snake_case__  )  )
												logger.warning(
												    f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
												    f''' {positive_arg}={kwargs[positive_arg]}'''  )

								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('torchscript'					,							self.torchscript  )
								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('torch_xla_tpu_print_metrics'					,							self.torch_xla_tpu_print_metrics  )
								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('fp16_opt_level'					,							self.fpaa_opt_level  )
								super().__init__(**snake_case__  )

						lowerCAmelCase__			      =field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Trace the models using torchscript"} )
						lowerCAmelCase__			      =field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Print Xla/PyTorch tpu metrics"} )
						lowerCAmelCase__			      =field(
						    default="O1"							,	metadata={
						        "help": (
						            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
						            "See details at https://nvidia.github.io/apex/amp.html"
						        )
						    }							,	)



						@cached_property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								requires_backends(self					,							['torch']  )
								logger.info('PyTorch: setting up devices'  )
								if not self.cuda:
										SCREAMING_SNAKE_CASE_					      =							torch.device('cpu'  )
										SCREAMING_SNAKE_CASE_					      =							0
								elif is_torch_tpu_available():
										SCREAMING_SNAKE_CASE_					      =							xm.xla_device()
										SCREAMING_SNAKE_CASE_					      =							0
								else:
										SCREAMING_SNAKE_CASE_					      =							torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
										SCREAMING_SNAKE_CASE_					      =							torch.cuda.device_count()
								return device, n_gpu



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return is_torch_tpu_available() and self.tpu



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								requires_backends(self					,							['torch']  )
								# TODO(PVP): currently only single GPU is supported
								return torch.cuda.current_device()



						@property
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								requires_backends(self					,							['torch']  )
								return self._setup_devices[0]



						@property
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								requires_backends(self					,							['torch']  )
								return self._setup_devices[1]






						@property
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								return self.n_gpu > 0





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


def 					_a							(					lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase )



class    lowercase_		:



						def __init__(			self	:   int					,							*,
						    snake_case__	:   float = np.inf					,							snake_case__	:   str = "linear"					,							snake_case__	:   float = 0.0					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							regularization
								SCREAMING_SNAKE_CASE_					      =							gamma
								if kernel == "linear":
										SCREAMING_SNAKE_CASE_					      =							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'  )
										SCREAMING_SNAKE_CASE_					      =							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:
										SCREAMING_SNAKE_CASE_					      =							f'''Unknown kernel: {kernel}'''
										raise ValueError(snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.dot(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.exp(-(self.gamma * norm_squared(vectora - vectora  ))  )



						def 	__a					(			self	:   int					,							snake_case__	:   list[ndarray]					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							observations
								SCREAMING_SNAKE_CASE_					      =							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

								((SCREAMING_SNAKE_CASE_)      ,				)					      =							np.shape(snake_case__  )

								def to_minimize(snake_case__	:   ndarray  ) -> float:
										SCREAMING_SNAKE_CASE_					      =							0
										((SCREAMING_SNAKE_CASE_)      ,				)					      =							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__  )

								SCREAMING_SNAKE_CASE_					      =							LinearConstraint(snake_case__					,							0					,							0  )
								SCREAMING_SNAKE_CASE_					      =							Bounds(0					,							self.regularization  )

								SCREAMING_SNAKE_CASE_					      =							minimize(
								    snake_case__					,							np.ones(snake_case__  )					,							bounds=snake_case__					,							constraints=[ly_contraint]  ).x
								SCREAMING_SNAKE_CASE_					      =							l_star

								# calculating mean offset of separation plane to points
								SCREAMING_SNAKE_CASE_					      =							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]  )
								SCREAMING_SNAKE_CASE_					      =							s / n






						def 	__a					(			self	:   Any					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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()