

# 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


lowercase_									=   '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      a      (			)    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =_ask_options(
          'In which compute environment are you running?'      ,   ['This machine', 'AWS (Amazon SageMaker)']      ,   _convert_compute_environment      ,   )
      if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
            _lowercase	       =get_sagemaker_input()
      else:
            _lowercase	       =get_cluster_input()
      return config
def      a      (			A__	:   Dict=None     )    ->      Tuple:





      """simple docstring"""



      if subparsers is not None:
            _lowercase	       =subparsers.add_parser('config'      ,   description=A__     )
      else:
            _lowercase	       =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      a      (			A__	:   Any     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =get_user_input()
      if args.config_file is not None:
            _lowercase	       =args.config_file
      else:
            if not os.path.isdir(A__     ):
                  os.makedirs(A__     )
            _lowercase	       =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      a      (			)    ->      int:





      """simple docstring"""



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


if __name__ == "__main__":
  main()





import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


