





"""simple docstring"""


# 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 subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCamelCase__				  =   "Run commands across TPU VMs for initial setup before running `accelerate launch`."




def 					lowercase__    (					lowercase_=None			)					->		Optional[int]:




      """simple docstring"""

      if subparsers is not None:
            _UpperCamelCase      :	str						=       subparsers.add_parser("tpu-config"		,description=_description			)
      else:
            _UpperCamelCase      :	str						=       argparse.ArgumentParser("Accelerate tpu-config command"		,description=_description			)
      # Core arguments
      _UpperCamelCase      :	Tuple						=       parser.add_argument_group(
          "Config Arguments"		,"Arguments that can be configured through `accelerate config`."			)
      config_args.add_argument(
          "--config_file"		,type=lowercase_		,default=lowercase_		,help="Path to the config file to use for accelerate."		,)
      config_args.add_argument(
          "--tpu_name"		,default=lowercase_		,help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,)
      config_args.add_argument(
          "--tpu_zone"		,default=lowercase_		,help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,)
      _UpperCamelCase      :	List[Any]						=       parser.add_argument_group("TPU Arguments"		,"Arguments for options ran inside the TPU."			)
      pod_args.add_argument(
          "--use_alpha"		,action="store_true"		,help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,)
      pod_args.add_argument(
          "--command_file"		,default=lowercase_		,help="The path to the file containing the commands to run on the pod on startup."		,)
      pod_args.add_argument(
          "--command"		,action="append"		,nargs="+"		,help="A command to run on the pod. Can be passed multiple times."		,)
      pod_args.add_argument(
          "--install_accelerate"		,action="store_true"		,help="Whether to install accelerate on the pod. Defaults to False."		,)
      pod_args.add_argument(
          "--accelerate_version"		,default="latest"		,help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,)
      pod_args.add_argument(
          "--debug"		,action="store_true"		,help="If set, will print the command that would be run instead of running it."			)

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




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       None

      # Get the default from the config file if it exists.
      if args.config_file is not None or os.path.isfile(lowercase_			):
            _UpperCamelCase      :	Optional[int]						=       load_config_from_file(args.config_file			)
            if not args.command_file and defaults.command_file is not None and not args.command:
                  _UpperCamelCase      :	Optional[Any]						=       defaults.command_file
            if not args.command and defaults.commands is not None:
                  _UpperCamelCase      :	List[str]						=       defaults.commands
            if not args.tpu_name:
                  _UpperCamelCase      :	Optional[Any]						=       defaults.tpu_name
            if not args.tpu_zone:
                  _UpperCamelCase      :	Optional[Any]						=       defaults.tpu_zone
      if args.accelerate_version == "dev":
            _UpperCamelCase      :	List[str]						=       "git+https://github.com/huggingface/accelerate.git"
      elif args.accelerate_version == "latest":
            _UpperCamelCase      :	int						=       "accelerate -U"
      elif isinstance(parse(args.accelerate_version			)		,lowercase_			):
            _UpperCamelCase      :	str						=       F'''accelerate=={args.accelerate_version}'''

      if not args.command_file and not args.command:
            raise ValueError("You must specify either a command file or a command to run on the pod."			)

      if args.command_file:
            with open(args.command_file		,"r"			) as f:
                  _UpperCamelCase      :	int						=       [f.read().splitlines()]

    # To turn list of lists into list of strings
      if isinstance(args.command[0]		,lowercase_			):
            _UpperCamelCase      :	Optional[int]						=       [line for cmd in args.command for line in cmd]
      # Default to the shared folder and install accelerate
      _UpperCamelCase      :	Optional[Any]						=       ["cd /usr/share"]
      if args.install_accelerate:
            new_cmd += [F'''pip install {args.accelerate_version}''']
      new_cmd += args.command
      _UpperCamelCase      :	Optional[int]						=       "; ".join(lowercase_			)

      # Then send it to gcloud
      # Eventually try to use google-api-core to do this instead of subprocess
      _UpperCamelCase      :	Any						=       ["gcloud"]
      if args.use_alpha:
            cmd += ["alpha"]
      cmd += [
          "compute",
          "tpus",
          "tpu-vm",
          "ssh",
          args.tpu_name,
          "--zone",
          args.tpu_zone,
          "--command",
          args.command,
          "--worker",
          "all",
      ]
      if args.debug:
            print(F'''Running {' '.join(lowercase_			)}'''			)
            return
      subprocess.run(lowercase_			)
      print("Successfully setup pod."			)




def 					lowercase__    (					)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	str						=       tpu_command_parser()
      _UpperCamelCase      :	str						=       parser.parse_args()

      tpu_command_launcher(lowercase_			)







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       0.0
        _UpperCamelCase      :	Union[str, Any]						=       0.0
        for i in range(len(__a	)	):
              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    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

      # 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()
