
"""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    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = botoa.client("iam"				)

  _SCREAMING_SNAKE_CASE   :				str							    = {
      "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				)				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {
        "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    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				Dict							    = botoa.client("iam"				)
  return iam_client.get_role(RoleName=lowerCamelCase__				)["Role"]["Arn"]





def    _lowerCAmelCase    (     )  ->    Any:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = _ask_options(
      "How do you want to authorize?",						["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "],						lowerCamelCase__,						)
  _SCREAMING_SNAKE_CASE   :				int							    = None
  if credentials_configuration == 0:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = _ask_field("Enter your AWS Profile name: [default] ",						default="default"				)
    _SCREAMING_SNAKE_CASE   :				int							    = 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   :				str							    = _ask_field("AWS Access Key ID: "				)
    _SCREAMING_SNAKE_CASE   :				int							    = aws_access_key_id

    _SCREAMING_SNAKE_CASE   :				int							    = _ask_field("AWS Secret Access Key: "				)
    _SCREAMING_SNAKE_CASE   :				Any							    = aws_secret_access_key

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

  _SCREAMING_SNAKE_CASE   :				Dict							    = _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:
    _SCREAMING_SNAKE_CASE   :				Dict							    = _ask_field("Enter your IAM role name: "				)
  else:
    _SCREAMING_SNAKE_CASE   :				str							    = "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__				)

  _SCREAMING_SNAKE_CASE   :				Dict							    = _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.",						)
  _SCREAMING_SNAKE_CASE   :				int							    = None
  if is_custom_docker_image:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = _ask_field("Enter your Docker image: ",						lambda lowerCamelCase__				: str(lowerCamelCase__				).lower()				)

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = _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.",						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = None
  if is_sagemaker_inputs_enabled:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = _ask_field(
        "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ",						lambda lowerCamelCase__				: str(lowerCamelCase__				).lower(),						)

  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = _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.",						)
  _SCREAMING_SNAKE_CASE   :				int							    = None
  if is_sagemaker_metrics_enabled:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = _ask_field(
        "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ",						lambda lowerCamelCase__				: str(lowerCamelCase__				).lower(),						)

  _SCREAMING_SNAKE_CASE   :				Any							    = _ask_options(
      "What is the distributed mode?",						["No distributed training", "Data parallelism"],						_convert_sagemaker_distributed_mode,						)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {}
  _SCREAMING_SNAKE_CASE   :				int							    = _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:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = "dynamo_"
    _SCREAMING_SNAKE_CASE   :				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,						)
    _SCREAMING_SNAKE_CASE   :				List[str]							    = _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:
      _SCREAMING_SNAKE_CASE   :				List[str]							    = _ask_options(
          "Which mode do you want to use?",						lowerCamelCase__,						lambda lowerCamelCase__				: TORCH_DYNAMO_MODES[int(lowerCamelCase__				)],						default="default",						)
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = _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.",						)
      _SCREAMING_SNAKE_CASE   :				Any							    = _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.",						)
  _SCREAMING_SNAKE_CASE   :				str							    = "Which EC2 instance type you want to use for your training?"
  if distributed_type != SageMakerDistributedType.NO:
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = _ask_options(
        lowerCamelCase__,						lowerCamelCase__,						lambda lowerCamelCase__				: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(lowerCamelCase__				)]				)
  else:
    eca_instance_query += "? [ml.p3.2xlarge]:"
    _SCREAMING_SNAKE_CASE   :				int							    = _ask_field(lowerCamelCase__,						lambda lowerCamelCase__				: str(lowerCamelCase__				).lower(),						default="ml.p3.2xlarge"				)

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

  _SCREAMING_SNAKE_CASE   :				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=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"""
from collections.abc import Iterable
from typing import Any
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = value
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None  # Added in order to delete a node easier
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None




     def __repr__(     self				):




       """simple docstring"""


       from pprint import pformat

       if self.left is None and self.right is None:
         return str(self.value				)
       return pformat({F'''{self.value}''': (self.left, self.right)}  ,     indent=1				)
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = root
     def __str__(     self				):




       """simple docstring"""


       return str(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if new_children is not None:  # reset its kids
         _SCREAMING_SNAKE_CASE   :				Dict							    = node.parent
       if node.parent is not None:  # reset its parent
         if self.is_right(snake_case__				):  # If it is the right children
           _SCREAMING_SNAKE_CASE   :				Any							    = new_children
         else:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_children
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = new_children
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node.parent and node.parent.right:
         return node == node.parent.right
       return False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.root is None
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Node(snake_case__				)  # create a new Node
       if self.empty():  # if Tree is empty
         _SCREAMING_SNAKE_CASE   :				str							    = new_node  # set its root
       else:  # Tree is not empty
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.root  # from root
         if parent_node is None:
           return
         while True:  # While we don't get to a leaf
           if value < parent_node.value:  # We go left
             if parent_node.left is None:
               _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_node  # We insert the new node in a leaf
               break
             else:
               _SCREAMING_SNAKE_CASE   :				int							    = parent_node.left
           else:
             if parent_node.right is None:
               _SCREAMING_SNAKE_CASE   :				str							    = new_node
               break
             else:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = parent_node.right
         _SCREAMING_SNAKE_CASE   :				Any							    = parent_node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__				):




       """simple docstring"""


       for value in values:
         self.__insert(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.empty():
         raise IndexError("Warning: Tree is empty! please use another."				)
       else:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.root
         # use lazy evaluation here to avoid NoneType Attribute error
         while node is not None and node.value is not value:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = node.left if value < node.value else node.right
         return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         if self.root is None:
           return None
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.root

       if not self.empty():
         while node.right is not None:
           _SCREAMING_SNAKE_CASE   :				Dict							    = node.right
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.root
       if self.root is None:
         return None
       if not self.empty():
         _SCREAMING_SNAKE_CASE   :				Any							    = self.root
         while node.left is not None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = node.left
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.search(snake_case__				)  # Look for the node with that label
       if node is not None:
         if node.left is None and node.right is None:  # If it has no children
           self.__reassign_nodes(snake_case__  ,     snake_case__				)
         elif node.left is None:  # Has only right children
           self.__reassign_nodes(snake_case__  ,     node.right				)
         elif node.right is None:  # Has only left children
           self.__reassign_nodes(snake_case__  ,     node.left				)
         else:
           _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_max(
               node.left				)  # Gets the max value of the left branch
           self.remove(tmp_node.value				)  # type: ignore
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = (
               tmp_node.value  # type: ignore
           )  # Assigns the value to the node to delete and keep tree structure
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node is not None:
         yield node  # Preorder Traversal
         yield from self.preorder_traverse(node.left				)
         yield from self.preorder_traverse(node.right				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None				):




       """simple docstring"""


       if traversal_function is None:
         return self.preorder_traverse(self.root				)
       else:
         return traversal_function(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if node:
         self.inorder(snake_case__  ,     node.left				)
         arr.append(node.value				)
         self.inorder(snake_case__  ,     node.right				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				list[int]							    = []
       self.inorder(snake_case__  ,     snake_case__				)  # append all values to list using inorder traversal
       return arr[k - 1]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Node | None				)  ->    list[Node]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  if curr_node is not None:
    _SCREAMING_SNAKE_CASE   :				int							    = postorder(curr_node.left				) + postorder(curr_node.right				) + [curr_node]
  return node_list





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Any							    = (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = BinarySearchTree()
  for i in testlist:
    t.insert(lowerCamelCase__				)

  # Prints all the elements of the list in order traversal
  print(lowerCamelCase__				)

  if t.search(6				) is not None:
    print("The value 6 exists"				)
  else:
    print("The value 6 doesn't exist"				)

  if t.search(-1				) is not None:
    print("The value -1 exists"				)
  else:
    print("The value -1 doesn't exist"				)

  if not t.empty():
    print("Max Value: ",						t.get_max().value				)  # type: ignore
    print("Min Value: ",						t.get_min().value				)  # type: ignore

  for i in testlist:
    t.remove(lowerCamelCase__				)
    print(lowerCamelCase__				)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



