




"""simple docstring"""

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 		_snake_case	(   _snake_case				:   Tuple				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  botoa.client('iam'				)

      _A     =  {
          '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=_lowerCamelCase							,    AssumeRolePolicyDocument=json.dumps(_lowerCamelCase							,    indent=2				)				)
            _A     =  {
                '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=_lowerCamelCase							,    PolicyName=F'''{role_name}_policy_permission'''							,    PolicyDocument=json.dumps(_lowerCamelCase							,    indent=2				)							,    )
      except iam_client.exceptions.EntityAlreadyExistsException:
            print(F'''role {role_name} already exists. Using existing one'''				)






def 		_snake_case	(   _snake_case				:   Any				)       ->   Any:






      '''simple docstring'''


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







def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


      _A     =  _ask_options(
          'How do you want to authorize?'							,    ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']							,    _lowerCamelCase							,    )
      _A     =  None
      if credentials_configuration == 0:
            _A     =  _ask_field('Enter your AWS Profile name: [default] '							,    default='default'				)
            _A     =  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`'				)
            _A     =  _ask_field('AWS Access Key ID: '				)
            _A     =  aws_access_key_id

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

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

      _A     =  _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']							,    _lowerCamelCase							,    )
      if role_management == 0:
            _A     =  _ask_field('Enter your IAM role name: '				)
      else:
            _A     =  'accelerate_sagemaker_execution_role'
            print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials'''				)
            _create_iam_role_for_sagemaker(_lowerCamelCase				)

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

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

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

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

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

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

      _A     =  _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=_lowerCamelCase							,    compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER							,    distributed_type=_lowerCamelCase							,    use_cpu=_lowerCamelCase							,    dynamo_config=_lowerCamelCase							,    eca_instance_type=_lowerCamelCase							,    profile=_lowerCamelCase							,    region=_lowerCamelCase							,    iam_role_name=_lowerCamelCase							,    mixed_precision=_lowerCamelCase							,    num_machines=_lowerCamelCase							,    sagemaker_inputs_file=_lowerCamelCase							,    sagemaker_metrics_file=_lowerCamelCase							,    )







'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	{
         '''^''': 3,
         '''*''': 2,
         '''/''': 2,
         '''%''': 2,
         '''+''': 1,
         '''-''': 1,
     }  # Priority of each operator
     __snake_case            =	len(_lowerCamelCase						) if (len(_lowerCamelCase						) > 7) else 7

     # Print table header for output
     print(
         '''Symbol'''.center(8						) ,						'''Stack'''.center(_lowerCamelCase						) ,						'''Postfix'''.center(_lowerCamelCase						) ,						sep=''' | ''' ,						)
     print('''-''' * (print_width * 3 + 7)						)

     for x in infix:
          if x.isalpha() or x.isdigit():
               post_fix.append(_lowerCamelCase						)  # if x is Alphabet / Digit, add it to Postfix
          elif x == "(":
               stack.append(_lowerCamelCase						)  # if x is "(" push to Stack
          elif x == ")":  # if x is ")" pop stack until "(" is encountered
               while stack[-1] != "(":
                    post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
               stack.pop()
          else:
               if len(_lowerCamelCase						) == 0:
                    stack.append(_lowerCamelCase						)  # If stack is empty, push x to stack
               else:  # while priority of x is not > priority of element in the stack
                    while len(_lowerCamelCase						) > 0 and priority[x] <= priority[stack[-1]]:
                         post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                    stack.append(_lowerCamelCase						)  # push x to stack

          print(
              x.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     while len(_lowerCamelCase						) > 0:  # while stack is not empty
          post_fix.append(stack.pop()						)  # pop stack & add to Postfix
          print(
              ''' '''.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     return "".join(_lowerCamelCase						)  # return Postfix as str







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	list(infix[::-1]						)  # reverse the infix equation

     for i in range(len(_lowerCamelCase						)						):
          if infix[i] == "(":
               __snake_case            =	''')'''  # change "(" to ")"
          elif infix[i] == ")":
               __snake_case            =	'''('''  # change ")" to "("

     return (infix_2_postfix(''''''.join(_lowerCamelCase						)						))[
         ::-1
     ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
     UpperCAmelCase_							:	Dict	     =		input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
     UpperCAmelCase_							:	Optional[Any]	     =		''''''.join(Infix.split())  # Remove spaces from the input
     print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')






