




from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class 			__magic_name__    :
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				int
				SCREAMING_SNAKE_CASE__       :				TreeNode | None						   =       None
				SCREAMING_SNAKE_CASE__       :				TreeNode | None						   =       None






__SCREAMING_SNAKE_CASE			    =namedtuple("""CoinsDistribResult""", """moves excess""")

def       a (_lowerCAmelCase  ):

							if root is None:
														return 0

							# Validation
							def count_nodes(_lowerCAmelCase  ) -> int:
														if node is None:
																					return 0

														return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

							def count_coins(_lowerCAmelCase  ) -> int:
														if node is None:
																					return 0

														return count_coins(node.left  ) + count_coins(node.right  ) + node.data

							if count_nodes(UpperCamelCase_  ) != count_coins(UpperCamelCase_  ):
														raise ValueError('''The nodes number should be same as the number of coins'''  )

							# Main calculation
							def get_distrib(_lowerCAmelCase  ) -> CoinsDistribResult:

														if node is None:
																					return CoinsDistribResult(0	,     1  )

														SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     get_distrib(node.left  )
														SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     get_distrib(node.right  )

														SCREAMING_SNAKE_CASE_ 						=     1 - left_distrib_excess
														SCREAMING_SNAKE_CASE_ 						=     1 - right_distrib_excess

														SCREAMING_SNAKE_CASE_ 						=     (
														    left_distrib_moves
														    + right_distrib_moves
														    + abs(UpperCamelCase_  )
														    + abs(UpperCamelCase_  )
														)
														SCREAMING_SNAKE_CASE_ 						=     node.data - coins_to_left - coins_to_right

														return CoinsDistribResult(UpperCamelCase_	,     UpperCamelCase_  )

							return get_distrib(UpperCamelCase_  )[0]


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       a_				(	UpperCamelCase_ ):
							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=UpperCamelCase_						,  AssumeRolePolicyDocument=json.dumps(UpperCamelCase_						,  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=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       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )
							return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]







def       a_				(	):
							A_			    =		_ask_options(
							    "How do you want to authorize?"						,  ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,  UpperCamelCase_						,  )
							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"]						,  UpperCamelCase_						,  )
							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(UpperCamelCase_ )

							A_			    =		_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."						,  )
							A_			    =		None
							if is_custom_docker_image:
														A_			    =		_ask_field("Enter your Docker image: "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )

							A_			    =		_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."						,  )
							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 UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_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."						,  )
							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 UpperCamelCase_ : str(UpperCamelCase_ ).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=UpperCamelCase_						,  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=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )

														if use_custom_options:
																					A_			    =		_ask_options(
																					    "Which mode do you want to use?"						,  UpperCamelCase_						,  lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )]						,  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=UpperCamelCase_						,  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=UpperCamelCase_						,  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(
														    UpperCamelCase_						,  UpperCamelCase_						,  lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
							else:
														eca_instance_query += "? [ml.p3.2xlarge]:"
														A_			    =		_ask_field(UpperCamelCase_						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).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]: "						,  UpperCamelCase_						,  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=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_						,  )





