




def 					lowerCamelCase__       (						A__       :					Dict	):





       '''simple docstring'''



       return [
           {
               0: [1, 2],
               1: [0, 2],
               2: [0, 1, 3, 5],
               3: [2, 4],
               4: [3],
               5: [2, 6, 8],
               6: [5, 7],
               7: [6, 8],
               8: [5, 7],
           },
           {
               0: [6],
               1: [9],
               2: [4, 5],
               3: [4],
               4: [2, 3],
               5: [2],
               6: [0, 7],
               7: [6],
               8: [],
               9: [1],
           },
           {
               0: [4],
               1: [6],
               2: [],
               3: [5, 6, 7],
               4: [0, 6],
               5: [3, 8, 9],
               6: [1, 3, 4, 7],
               7: [3, 6, 8, 9],
               8: [5, 7],
               9: [5, 7],
           },
           {
               0: [1, 3],
               1: [0, 2, 4],
               2: [1, 3, 4],
               3: [0, 2, 4],
               4: [1, 2, 3],
           },
       ][index]




def 					lowerCamelCase__       (						A__       :					dict[int, list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						0
       __lowerCamelCase   =						len(_UpperCamelCase	)  # No of vertices in graph
       __lowerCamelCase   =						[0] * n
       __lowerCamelCase   =						[False] * n

       def dfs(A__       :					int  ,			A__       :					Optional[int]  ,			A__       :					Optional[int]  ,			A__       :					Dict	):
              __lowerCamelCase   =						True
              __lowerCamelCase   =						id_
              id_ += 1
              for to in graph[at]:
                     if to == parent:
                            pass
                     elif not visited[to]:
                            dfs(_UpperCamelCase  ,			_UpperCamelCase  ,			_UpperCamelCase  ,			id_	)
                            __lowerCamelCase   =						min(low[at]  ,			low[to]	)
                            if id_ <= low[to]:
                                   bridges.append((at, to) if at < to else (to, at)	)
                     else:
                            # This edge is a back edge and cannot be a bridge
                            __lowerCamelCase   =						min(low[at]  ,			low[to]	)

       __lowerCamelCase   =						[]
       for i in range(_UpperCamelCase	):
              if not visited[i]:
                     dfs(_UpperCamelCase  ,			-1  ,			_UpperCamelCase  ,			id_	)
       return bridges


if __name__ == "__main__":
       import doctest

       doctest.testmod()




'''simple docstring'''
# 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 json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
     import botoa  # noqa: F401


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =botoa.client('iam'				)

     _SCREAMING_SNAKE_CASE							    ={
         'Version': '2012-10-17',
         'Statement': [
             {'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
         ],
     }
     try:
          # create the role, associated with the chosen trust policy
          iam_client.create_role(
              RoleName=_UpperCamelCase							, AssumeRolePolicyDocument=json.dumps(_UpperCamelCase							, indent=2				)				)
          _SCREAMING_SNAKE_CASE							    ={
              'Version': '2012-10-17',
              'Statement': [
                  {
                      'Effect': 'Allow',
                      'Action': [
                          'sagemaker:*',
                          'ecr:GetDownloadUrlForLayer',
                          'ecr:BatchGetImage',
                          'ecr:BatchCheckLayerAvailability',
                          'ecr:GetAuthorizationToken',
                          'cloudwatch:PutMetricData',
                          'cloudwatch:GetMetricData',
                          'cloudwatch:GetMetricStatistics',
                          'cloudwatch:ListMetrics',
                          'logs:CreateLogGroup',
                          'logs:CreateLogStream',
                          'logs:DescribeLogStreams',
                          'logs:PutLogEvents',
                          'logs:GetLogEvents',
                          's3:CreateBucket',
                          's3:ListBucket',
                          's3:GetBucketLocation',
                          's3:GetObject',
                          's3:PutObject',
                      ],
                      'Resource': '*',
                  }
              ],
          }
          # attach policy to role
          iam_client.put_role_policy(
              RoleName=_UpperCamelCase							, PolicyName=f"{role_name}_policy_permission"							, PolicyDocument=json.dumps(_UpperCamelCase							, indent=2				)							, )
     except iam_client.exceptions.EntityAlreadyExistsException:
          print(f"role {role_name} already exists. Using existing one"				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =botoa.client('iam'				)
     return iam_client.get_role(RoleName=_UpperCamelCase				)["Role"]["Arn"]







def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =_ask_options(
         'How do you want to authorize?'							, ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']							, _UpperCamelCase							, )
     _SCREAMING_SNAKE_CASE							    =None
     if credentials_configuration == 0:
          _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your AWS Profile name: [default] '							, default='default'				)
          _SCREAMING_SNAKE_CASE							    =aws_profile
     else:
          print(
              'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
              '`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'				)
          _SCREAMING_SNAKE_CASE							    =_ask_field('AWS Access Key ID: '				)
          _SCREAMING_SNAKE_CASE							    =aws_access_key_id

          _SCREAMING_SNAKE_CASE							    =_ask_field('AWS Secret Access Key: '				)
          _SCREAMING_SNAKE_CASE							    =aws_secret_access_key

     _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your AWS Region: [us-east-1]'							, default='us-east-1'				)
     _SCREAMING_SNAKE_CASE							    =aws_region

     _SCREAMING_SNAKE_CASE							    =_ask_options(
         'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'							, ['Provide IAM Role name', 'Create new IAM role using credentials']							, _UpperCamelCase							, )
     if role_management == 0:
          _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your IAM role name: '				)
     else:
          _SCREAMING_SNAKE_CASE							    ='accelerate_sagemaker_execution_role'
          print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"				)
          _create_iam_role_for_sagemaker(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =_ask_field(
         'Do you want to use custom Docker image? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_custom_docker_image:
          _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your Docker image: '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()				)

     _SCREAMING_SNAKE_CASE							    =_ask_field(
         'Do you want to provide SageMaker input channels with data locations? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_sagemaker_inputs_enabled:
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()							, )

     _SCREAMING_SNAKE_CASE							    =_ask_field(
         'Do you want to enable SageMaker metrics? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_sagemaker_metrics_enabled:
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()							, )

     _SCREAMING_SNAKE_CASE							    =_ask_options(
         'What is the distributed mode?'							, ['No distributed training', 'Data parallelism']							, _convert_sagemaker_distributed_mode							, )
     _SCREAMING_SNAKE_CASE							    ={}
     _SCREAMING_SNAKE_CASE							    =_ask_field(
         'Do you wish to optimize your script with torch dynamo?[yes/NO]:'							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
     if use_dynamo:
          _SCREAMING_SNAKE_CASE							    ='dynamo_'
          _SCREAMING_SNAKE_CASE							    =_ask_options(
              'Which dynamo backend would you like to use?'							, [x.lower() for x in DYNAMO_BACKENDS]							, _convert_dynamo_backend							, default=2							, )
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'Do you want to customize the defaults sent to torch.compile? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )

          if use_custom_options:
               _SCREAMING_SNAKE_CASE							    =_ask_options(
                   'Which mode do you want to use?'							, _UpperCamelCase							, lambda _UpperCamelCase				: TORCH_DYNAMO_MODES[int(_UpperCamelCase				)]							, default='default'							, )
               _SCREAMING_SNAKE_CASE							    =_ask_field(
                   'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
               _SCREAMING_SNAKE_CASE							    =_ask_field(
                   'Do you want to enable dynamic shape tracing? [yes/NO]: '							, _convert_yes_no_to_bool							, default=_UpperCamelCase							, error_message='Please enter yes or no.'							, )
     _SCREAMING_SNAKE_CASE							    ='Which EC2 instance type you want to use for your training?'
     if distributed_type != SageMakerDistributedType.NO:
          _SCREAMING_SNAKE_CASE							    =_ask_options(
              _UpperCamelCase							, _UpperCamelCase							, lambda _UpperCamelCase				: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_UpperCamelCase				)]				)
     else:
          eca_instance_query += "? [ml.p3.2xlarge]:"
          _SCREAMING_SNAKE_CASE							    =_ask_field(_UpperCamelCase							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()							, default='ml.p3.2xlarge'				)

     _SCREAMING_SNAKE_CASE							    =1
     if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'How many machines do you want use? [1]: '							, _UpperCamelCase							, default=1							, )

     _SCREAMING_SNAKE_CASE							    =_ask_options(
         'Do you wish to use FP16 or BF16 (mixed precision)?'							, ['no', 'fp16', 'bf16', 'fp8']							, _convert_mixed_precision							, )

     if use_dynamo and mixed_precision == "no":
          print(
              'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'				)

     return SageMakerConfig(
         image_uri=_UpperCamelCase							, compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER							, distributed_type=_UpperCamelCase							, use_cpu=_UpperCamelCase							, dynamo_config=_UpperCamelCase							, eca_instance_type=_UpperCamelCase							, profile=_UpperCamelCase							, region=_UpperCamelCase							, iam_role_name=_UpperCamelCase							, mixed_precision=_UpperCamelCase							, num_machines=_UpperCamelCase							, sagemaker_inputs_file=_UpperCamelCase							, sagemaker_metrics_file=_UpperCamelCase							, )
