
# 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
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase_							=     """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""






def 						__magic_name__ (    ):



     '''simple docstring'''




     UpperCamelCase__				=				_ask_options(
         """In which compute environment are you running?"""       ,		["""This machine""", """AWS (Amazon SageMaker)"""]       ,		_convert_compute_environment       ,		)
     if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
          UpperCamelCase__				=				get_sagemaker_input()
     else:
          UpperCamelCase__				=				get_cluster_input()
     return config






def 						__magic_name__ (    __a			:       Any=None						):



     '''simple docstring'''




     if subparsers is not None:
          UpperCamelCase__				=				subparsers.add_parser("""config"""       ,		description=A__						)
     else:
          UpperCamelCase__				=				argparse.ArgumentParser("""Accelerate config command"""       ,		description=A__						)

     parser.add_argument(
         """--config_file"""       ,		default=A__       ,		help=(
             """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
             """location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
             """such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
             """with \'huggingface\'."""
         )       ,		)

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






def 						__magic_name__ (    __a			:       Tuple						):



     '''simple docstring'''




     UpperCamelCase__				=				get_user_input()
     if args.config_file is not None:
          UpperCamelCase__				=				args.config_file
     else:
          if not os.path.isdir(A__						):
               os.makedirs(A__						)
          UpperCamelCase__				=				default_yaml_config_file

     if config_file.endswith(""".json"""						):
          config.to_json_file(A__						)
     else:
          config.to_yaml_file(A__						)
     print(f"accelerate configuration saved at {config_file}"						)






def 						__magic_name__ (    ):



     '''simple docstring'''




     UpperCamelCase__				=				config_command_parser()
     UpperCamelCase__				=				parser.parse_args()
     config_command(A__						)


if __name__ == "__main__":
    main()












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



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				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__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[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)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				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])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	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()


