


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




def       a_ (       SCREAMING_SNAKE_CASE__   : ndarray	):
       '''simple docstring'''





       return np.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




class A    :






      def __init__(   self		:	List[str]     ,  *,
          lowercase_		:	float = np.inf     ,  lowercase_		:	str = "linear"     ,  lowercase_		:	float = 0.0     ,  )   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						Any            =regularization
             _lowerCamelCase			:						Dict            =gamma
             if kernel == "linear":
                    _lowerCamelCase			:						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'					)
                    _lowerCamelCase			:						int            =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:
                    _lowerCamelCase			:						Optional[int]            =F'''Unknown kernel: {kernel}'''
                    raise ValueError(lowercase_					)






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	ndarray     ,  lowercase_		:	ndarray					)   ->		float:
             """simple docstring"""




             return np.dot(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ndarray     ,  lowercase_		:	ndarray					)   ->		float:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	list[ndarray]     ,  lowercase_		:	ndarray					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						str            =observations
             _lowerCamelCase			:						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

             ((_lowerCamelCase)      ,	)			:						Union[str, Any]            =np.shape(lowercase_					)

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

             _lowerCamelCase			:						Union[str, Any]            =LinearConstraint(lowercase_     ,  0     ,  0					)
             _lowerCamelCase			:						Optional[int]            =Bounds(0     ,  self.regularization					)

             _lowerCamelCase			:						List[str]            =minimize(
                 lowercase_     ,  np.ones(lowercase_					)     ,  bounds=lowercase_     ,  constraints=[ly_contraint]					).x
             _lowerCamelCase			:						List[str]            =l_star

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






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	ndarray					)   ->		int:
             """simple docstring"""




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


if __name__ == "__main__":
 import doctest

 doctest.testmod()






# Copyright 2022 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
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       a_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

       parser.add_argument(
           '--config_file'						,     default=SCREAMING_SNAKE_CASE__						,     help='The config file to use for the default values in the launching script.'	)

       if subparsers is not None:
              parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
       return parser




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`Accelerate` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'Numpy version': np.__version__,
           'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
           'PyTorch XPU available': str(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =torch.cuda.get_device_name()

       print('\nCopy-and-paste the text below in your GitHub issue\n'	)
       print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]	)	)

       print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


if __name__ == "__main__":
 raise SystemExit(main())


