



# 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


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





def 							A     (					_UpperCAmelCase    :       Optional[Any]=None   )    ->			Union[str, Any]:







			'''simple docstring'''
			if subparsers is not None:
						_UpperCAmelCase			=       subparsers.add_parser('tpu-config'							,       description=_description   )
			else:
						_UpperCAmelCase			=       argparse.ArgumentParser('Accelerate tpu-config command'							,       description=_description   )
			# Core arguments
			_UpperCAmelCase			=       parser.add_argument_group(
			    'Config Arguments'							,       'Arguments that can be configured through `accelerate config`.'   )
			config_args.add_argument(
			    '--config_file'							,       type=_UpperCAmelCase							,       default=_UpperCAmelCase							,       help='Path to the config file to use for accelerate.'							,       )
			config_args.add_argument(
			    '--tpu_name'							,       default=_UpperCAmelCase							,       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=_UpperCAmelCase							,       help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'							,       )
			_UpperCAmelCase			=       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=_UpperCAmelCase							,       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=_UpperCAmelCase   )
			return parser





def 							A     (					_UpperCAmelCase    :       Tuple   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       None

			# Get the default from the config file if it exists.
			if args.config_file is not None or os.path.isfile(_UpperCAmelCase   ):
						_UpperCAmelCase			=       load_config_from_file(args.config_file   )
						if not args.command_file and defaults.command_file is not None and not args.command:
									_UpperCAmelCase			=       defaults.command_file
						if not args.command and defaults.commands is not None:
									_UpperCAmelCase			=       defaults.commands
						if not args.tpu_name:
									_UpperCAmelCase			=       defaults.tpu_name
						if not args.tpu_zone:
									_UpperCAmelCase			=       defaults.tpu_zone
			if args.accelerate_version == "dev":
						_UpperCAmelCase			=       'git+https://github.com/huggingface/accelerate.git'
			elif args.accelerate_version == "latest":
						_UpperCAmelCase			=       'accelerate -U'
			elif isinstance(parse(args.accelerate_version   )							,       _UpperCAmelCase   ):
						_UpperCAmelCase			=       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			=       [f.read().splitlines()]

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

			# Then send it to gcloud
			# Eventually try to use google-api-core to do this instead of subprocess
			_UpperCAmelCase			=       ['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(_UpperCAmelCase   )}"   )
						return
			subprocess.run(_UpperCAmelCase   )
			print('Successfully setup pod.'   )





def 							A     (					)    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       tpu_command_parser()
			_UpperCAmelCase			=       parser.parse_args()

			tpu_command_launcher(_UpperCAmelCase   )





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
