






import math




class     _lowerCAmelCase:





       """simple docstring"""







       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    0.0
         UpperCamelCase_: List[str]   				=    0.0
         for i in range(len(_lowerCamelCase							)							):
           da += math.pow((sample[i] - weights[0][i])  ,      2							)
           da += math.pow((sample[i] - weights[1][i])  ,      2							)
           return 0 if da > da else 1
         return 0





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         for i in range(len(_lowerCamelCase							)							):
           weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights







def   snake_case ()	->    None:
  # Training Examples ( m, n )
  UpperCamelCase_: Optional[Any]   				=    [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  UpperCamelCase_: int   				=    [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  UpperCamelCase_: List[str]   				=    SelfOrganizingMap()
  UpperCamelCase_: Union[str, Any]   				=    3
  UpperCamelCase_: List[str]   				=    0.5

  for _ in range(UpperCAmelCase__  ):
    for j in range(len(UpperCAmelCase__  )  ):
      # training sample
      UpperCamelCase_: List[str]   				=    training_samples[j]

      # Compute the winning vector
      UpperCamelCase_: List[Any]   				=    self_organizing_map.get_winner(UpperCAmelCase__     ,   UpperCAmelCase__  )

      # Update the winning vector
      UpperCamelCase_: str   				=    self_organizing_map.update(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

    # classify test sample
  UpperCamelCase_: Optional[Any]   				=    [0, 0, 0, 1]
  UpperCamelCase_: int   				=    self_organizing_map.get_winner(UpperCAmelCase__     ,   UpperCAmelCase__  )

  # results
  print(F'''Clusters that the test sample belongs to : {winner}'''  )
  print(F'''Weights that have been trained : {weights}'''  )


# running the main() function
if __name__ == "__main__":
   main()







# 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


A_		:		Any		     =			'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   snake_case ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   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=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

  if config_file.endswith('.json'  ):
    config.to_json_file(UpperCAmelCase__  )
  else:
    config.to_yaml_file(UpperCAmelCase__  )
  print(F'''accelerate configuration saved at {config_file}'''  )

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()