




'''simple docstring'''






A_			      =		"Alexander Joslin"

import operator as op

from .stack import Stack






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   str	      =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      snake_case__       :   Stack[int]	      =    Stack()
      snake_case__       :   Stack[str]	      =    Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(__SCREAMING_SNAKE_CASE		)		)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(__SCREAMING_SNAKE_CASE		)
            elif i == ")":
                  # RULE 4
                  snake_case__       :   Optional[int]	      =    operator_stack.peek()
                  operator_stack.pop()
                  snake_case__       :   Optional[Any]	      =    operand_stack.peek()
                  operand_stack.pop()
                  snake_case__       :   List[Any]	      =    operand_stack.peek()
                  operand_stack.pop()

                  snake_case__       :   int	      =    operators[opr](__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
                  operand_stack.push(__SCREAMING_SNAKE_CASE		)

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      A_			      =		"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(F'{equation} = {dijkstras_two_stack_algorithm(equation)}')






'''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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Optional[int]	      =    botoa.client('iam'		)

      snake_case__       :   Any	      =    {
          '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=__SCREAMING_SNAKE_CASE				,      AssumeRolePolicyDocument=json.dumps(__SCREAMING_SNAKE_CASE				,      indent=2		)		)
            snake_case__       :   str	      =    {
                '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=__SCREAMING_SNAKE_CASE				,      PolicyName=f"{role_name}_policy_permission"				,      PolicyDocument=json.dumps(__SCREAMING_SNAKE_CASE				,      indent=2		)				,      )
      except iam_client.exceptions.EntityAlreadyExistsException:
            print(f"role {role_name} already exists. Using existing one"		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    botoa.client('iam'		)
      return iam_client.get_role(RoleName=__SCREAMING_SNAKE_CASE		)["Role"]["Arn"]






def 		UpperCamelCase__      (		)    ->   Tuple:
      snake_case__       :   Dict	      =    _ask_options(
          'How do you want to authorize?'				,      ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']				,      __SCREAMING_SNAKE_CASE				,      )
      snake_case__       :   int	      =    None
      if credentials_configuration == 0:
            snake_case__       :   Any	      =    _ask_field('Enter your AWS Profile name: [default] '				,      default='default'		)
            snake_case__       :   Union[str, Any]	      =    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`'		)
            snake_case__       :   Dict	      =    _ask_field('AWS Access Key ID: '		)
            snake_case__       :   Tuple	      =    aws_access_key_id

            snake_case__       :   int	      =    _ask_field('AWS Secret Access Key: '		)
            snake_case__       :   Optional[int]	      =    aws_secret_access_key

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

      snake_case__       :   Optional[int]	      =    _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']				,      __SCREAMING_SNAKE_CASE				,      )
      if role_management == 0:
            snake_case__       :   Optional[int]	      =    _ask_field('Enter your IAM role name: '		)
      else:
            snake_case__       :   Dict	      =    'accelerate_sagemaker_execution_role'
            print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"		)
            _create_iam_role_for_sagemaker(__SCREAMING_SNAKE_CASE		)

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

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

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

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

            if use_custom_options:
                  snake_case__       :   int	      =    _ask_options(
                      'Which mode do you want to use?'				,      __SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: TORCH_DYNAMO_MODES[int(__SCREAMING_SNAKE_CASE		)]				,      default='default'				,      )
                  snake_case__       :   Dict	      =    _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=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
                  snake_case__       :   List[Any]	      =    _ask_field(
                      'Do you want to enable dynamic shape tracing? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      snake_case__       :   int	      =    'Which EC2 instance type you want to use for your training?'
      if distributed_type != SageMakerDistributedType.NO:
            snake_case__       :   Tuple	      =    _ask_options(
                __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__SCREAMING_SNAKE_CASE		)]		)
      else:
            eca_instance_query += "? [ml.p3.2xlarge]:"
            snake_case__       :   Dict	      =    _ask_field(__SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: str(__SCREAMING_SNAKE_CASE		).lower()				,      default='ml.p3.2xlarge'		)

      snake_case__       :   Optional[Any]	      =    1
      if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
            snake_case__       :   Union[str, Any]	      =    _ask_field(
                'How many machines do you want use? [1]: '				,      __SCREAMING_SNAKE_CASE				,      default=1				,      )

      snake_case__       :   Optional[int]	      =    _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=__SCREAMING_SNAKE_CASE				,      compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER				,      distributed_type=__SCREAMING_SNAKE_CASE				,      use_cpu=__SCREAMING_SNAKE_CASE				,      dynamo_config=__SCREAMING_SNAKE_CASE				,      eca_instance_type=__SCREAMING_SNAKE_CASE				,      profile=__SCREAMING_SNAKE_CASE				,      region=__SCREAMING_SNAKE_CASE				,      iam_role_name=__SCREAMING_SNAKE_CASE				,      mixed_precision=__SCREAMING_SNAKE_CASE				,      num_machines=__SCREAMING_SNAKE_CASE				,      sagemaker_inputs_file=__SCREAMING_SNAKE_CASE				,      sagemaker_metrics_file=__SCREAMING_SNAKE_CASE				,      )
