
'''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    UpperCAmelCase_						(__a :       Optional[Any]					):






   """simple docstring"""

   _a							:      List[Any]     =  botoa.client('iam'					)

   _a							:      Optional[int]     =  {
       '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=_snake_case					,      AssumeRolePolicyDocument=json.dumps(_snake_case					,      indent=2					)					)
      _a							:      Dict     =  {
          '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=_snake_case					,      PolicyName=f"""{role_name}_policy_permission"""					,      PolicyDocument=json.dumps(_snake_case					,      indent=2					)					,      )
   except iam_client.exceptions.EntityAlreadyExistsException:
      print(f"""role {role_name} already exists. Using existing one"""					)





def    UpperCAmelCase_						(__a :       List[Any]					):






   """simple docstring"""

   _a							:      Dict     =  botoa.client('iam'					)
   return iam_client.get_role(RoleName=_snake_case					)["Role"]["Arn"]





def    UpperCAmelCase_						():






   """simple docstring"""

   _a							:      Tuple     =  _ask_options(
       'How do you want to authorize?'					,      ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']					,      _snake_case					,      )
   _a							:      str     =  None
   if credentials_configuration == 0:
      _a							:      str     =  _ask_field('Enter your AWS Profile name: [default] '					,      default='default'					)
      _a							:      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`'					)
      _a							:      int     =  _ask_field('AWS Access Key ID: '					)
      _a							:      List[Any]     =  aws_access_key_id

      _a							:      List[Any]     =  _ask_field('AWS Secret Access Key: '					)
      _a							:      Optional[Any]     =  aws_secret_access_key

   _a							:      Tuple     =  _ask_field('Enter your AWS Region: [us-east-1]'					,      default='us-east-1'					)
   _a							:      Union[str, Any]     =  aws_region

   _a							:      Tuple     =  _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']					,      _snake_case					,      )
   if role_management == 0:
      _a							:      Tuple     =  _ask_field('Enter your IAM role name: '					)
   else:
      _a							:      List[Any]     =  'accelerate_sagemaker_execution_role'
      print(f"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""					)
      _create_iam_role_for_sagemaker(_snake_case					)

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

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

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

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

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

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

   _a							:      List[Any]     =  _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=_snake_case					,      compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER					,      distributed_type=_snake_case					,      use_cpu=_snake_case					,      dynamo_config=_snake_case					,      eca_instance_type=_snake_case					,      profile=_snake_case					,      region=_snake_case					,      iam_role_name=_snake_case					,      mixed_precision=_snake_case					,      num_machines=_snake_case					,      sagemaker_inputs_file=_snake_case					,      sagemaker_metrics_file=_snake_case					,      )





"""simple docstring"""



from __future__ import annotations

import queue





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Optional[int]     							=					None




def 					lowerCamelCase   (     ):
    print('\n********Press N to stop entering at any point of time********\n' )
    UpperCAmelCase__	:      int     							=					input('Enter the value of the root node: ' ).strip().lower()
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    UpperCAmelCase__	:      Dict     							=					TreeNode(int(_snake_case ) )
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Tuple     							=					q.get()
        UpperCAmelCase__	:      List[Any]     							=					F'''Enter the left node of {node_found.data}: '''
        UpperCAmelCase__	:      Dict     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      Optional[int]     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Tuple     							=					left_node
        q.put(_snake_case )
        UpperCAmelCase__	:      int     							=					F'''Enter the right node of {node_found.data}: '''
        UpperCAmelCase__	:      int     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      int     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Any     							=					right_node
        q.put(_snake_case )
    raise
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    print(node.data    ,end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    in_order(node.left )
    print(node.data    ,end=',' )
    in_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data    ,end=',' )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      int     							=					q.get()
        print(node_dequeued.data    ,end=',' )
        if node_dequeued.left:
            q.put(node_dequeued.left )
        if node_dequeued.right:
            q.put(node_dequeued.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Optional[Any]     							=					[]
        while not q.empty():
            UpperCAmelCase__	:      Union[str, Any]     							=					q.get()
            print(node_dequeued.data    ,end=',' )
            if node_dequeued.left:
                list_.append(node_dequeued.left )
            if node_dequeued.right:
                list_.append(node_dequeued.right )
        print()
        for node in list_:
            q.put(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[int]     							=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data    ,end=',' )
            stack.append(_snake_case )
            UpperCAmelCase__	:      Optional[int]     							=					n.left
        # end of while means current node doesn't have left child
        UpperCAmelCase__	:      Union[str, Any]     							=					stack.pop()
        # start to traverse its right child
        UpperCAmelCase__	:      Dict     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[Any]     							=					node
    while n or stack:
        while n:
            stack.append(_snake_case )
            UpperCAmelCase__	:      int     							=					n.left
        UpperCAmelCase__	:      Tuple     							=					stack.pop()
        print(n.data    ,end=',' )
        UpperCAmelCase__	:      Tuple     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    UpperCAmelCase__	:      Union[str, Any]     							=					node
    stacka.append(_snake_case )
    while stacka:  # to find the reversed order of post order, store it in stack2
        UpperCAmelCase__	:      Tuple     							=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(_snake_case )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data    ,end=',' )
def 					lowerCamelCase   (     _snake_case = ""    ,_snake_case=50    ,_snake_case="*" ):
    if not s:
        return "\n" + width * char
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					divmod(width - len(_snake_case ) - 2    ,2 )
    return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(prompt('Binary Tree Traversals'))

    UpperCamelCase__          =	build_tree()
    print(prompt('Pre Order Traversal'))
    pre_order(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal'))
    in_order(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal'))
    post_order(node)
    print(prompt() + '\n')

    print(prompt('Level Order Traversal'))
    level_order(node)
    print(prompt() + '\n')

    print(prompt('Actual Level Order Traversal'))
    level_order_actual(node)
    print('*' * 50 + '\n')

    print(prompt('Pre Order Traversal - Iteration Version'))
    pre_order_iter(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal - Iteration Version'))
    in_order_iter(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal - Iteration Version'))
    post_order_iter(node)
    print(prompt())

