


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


def 				lowerCamelCase__	(      a	:   ndarray				)    ->	float:





			"""simple docstring"""
			return np.dot(a	,  a				)


class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]   ,     *,
						    __A						:   float = np.inf   ,     __A						:   str = "linear"   ,     __A						:   float = 0.0   ,     )      ->None:






									"""simple docstring"""






									a__ :Optional[int]						  =    regularization
									a__ :Optional[int]						  =    gamma
									if kernel == "linear":
												a__ :Optional[int]						  =    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__ :List[Any]						  =    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__ :List[Any]						  =    F'''Unknown kernel: {kernel}'''
												raise ValueError(__A						)




						def  _snake_case					(	self						:   Any   ,     __A						:   ndarray   ,     __A						:   ndarray						)      ->float:






									"""simple docstring"""






									return np.dot(__A   ,     __A						)




						def  _snake_case					(	self						:   int   ,     __A						:   ndarray   ,     __A						:   ndarray						)      ->float:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Any   ,     __A						:   list[ndarray]   ,     __A						:   ndarray						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    observations
									a__ :Union[str, 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__)  ,  ) :Dict						  =    np.shape(__A						)

									def to_minimize(__A						:   ndarray						) -> float:
												a__ :Optional[int]						  =    0
												((a__)  ,  ) :Union[str, Any]						  =    np.shape(__A						)
												for i in range(__A						):
															for j in range(__A						):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]   ,     observations[j]						)
																		)
												return 1 / 2 * s - sum(__A						)

									a__ :List[str]						  =    LinearConstraint(__A   ,     0   ,     0						)
									a__ :Tuple						  =    Bounds(0   ,     self.regularization						)

									a__ :Optional[Any]						  =    minimize(
									    __A   ,     np.ones(__A						)   ,     bounds=__A   ,     constraints=[ly_contraint]						).x
									a__ :int						  =    l_star

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






						def  _snake_case					(	self						:   List[str]   ,     __A						:   ndarray						)      ->int:






									"""simple docstring"""






									a__ :Optional[Any]						  =    sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]   ,     __A						)
									    for n in range(len(self.classes						)						)						)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


snake_case__			 =						[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple=None	,  a	:   int=None	,  a	:   List[str]=None				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    True
			while ask_again:
						a__ :str						  =    input(a				)
						try:
									if default is not None and len(a				) == 0:
												return default
									return convert_value(a				) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]=[]	,  a	:   str=None	,  a	:   str=0				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    BulletMenu(a	,  a				)
			a__ :int						  =    menu.run(default_choice=a				)
			return convert_value(a				) if convert_value is not None else result


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    int(a				)
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    int(a				)
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :int						  =    int(a				)
			return DynamoBackend(DYNAMO_BACKENDS[value]				).value


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





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	List[str]:





			"""simple docstring"""
			return {"yes": True, "no": False}[value.lower()]







class 					lowerCAmelCase_ (       argparse.RawDescriptionHelpFormatter):




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Tuple   ,     __A						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    super()._format_usage(__A   ,     __A   ,     __A   ,     __A						)
									a__ :Any						  =    usage.replace("<command> [<args>] "   ,     ""						)
									return usage


