



from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 					snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	42
     _SCREAMING_SNAKE_CASE						=	42




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  List[str], _snake_case  :  int					)   ->Dict:
            snake_case__							:	list[list[Edge]]   	=	[[] for _ in range(_snake_case					)]
            snake_case__							:	str   	=	size






     def __getitem__(       self  :  List[str], _snake_case  :  int					)   ->Iterator[Edge]:
            return iter(self._graph[vertex]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->List[Any]:
            return self._size






     def    lowercase_   (       self  :  Tuple, _snake_case  :  int, _snake_case  :  int, _snake_case  :  int					)   ->List[Any]:
            if weight not in (0, 1):
                   raise ValueError('Edge weight must be either 0 or 1.'					)

            if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError('Vertex indexes must be in [0; size).'					)

            self._graph[from_vertex].append(Edge(_snake_case, _snake_case					)					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  int, _snake_case  :  int					)   ->int | None:
            snake_case__							:	Dict   	=	deque([start_vertex]					)
            snake_case__							:	list[int | None]   	=	[None] * self.size
            snake_case__							:	int   	=	0

            while queue:
                   snake_case__							:	str   	=	queue.popleft()
                   snake_case__							:	List[Any]   	=	distances[current_vertex]
                   if current_distance is None:
                          continue

                   for edge in self[current_vertex]:
                          snake_case__							:	Any   	=	current_distance + edge.weight
                          snake_case__							:	List[Any]   	=	distances[edge.destination_vertex]
                          if (
                              isinstance(_snake_case, _snake_case					)
                              and new_distance >= dest_vertex_distance
                          ):
                                 continue
                          snake_case__							:	List[Any]   	=	new_distance
                          if edge.weight == 0:
                                 queue.appendleft(edge.destination_vertex					)
                          else:
                                 queue.append(edge.destination_vertex					)

            if distances[finish_vertex] is None:
                   raise ValueError('No path from start_vertex to finish_vertex.'					)

            return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()













# 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.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





