

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	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= botoa.client("iam"      )

	UpperCAmelCase__:	Optional[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=_lowerCAmelCase	,AssumeRolePolicyDocument=json.dumps(_lowerCAmelCase	,indent=2      )      )
		UpperCAmelCase__:	Optional[Any]									= {
		    "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 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Any									= botoa.client("iam"      )
	return iam_client.get_role(RoleName=_lowerCAmelCase      )["Role"]["Arn"]



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

		UpperCAmelCase__:	Tuple									= _ask_field("AWS Secret Access Key: "      )
		UpperCAmelCase__:	Dict									= aws_secret_access_key

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

	UpperCAmelCase__:	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"]	,_lowerCAmelCase	,)
	if role_management == 0:
		UpperCAmelCase__:	str									= _ask_field("Enter your IAM role name: "      )
	else:
		UpperCAmelCase__:	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      )

	UpperCAmelCase__:	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."	,)
	UpperCAmelCase__:	Optional[Any]									= None
	if is_custom_docker_image:
		UpperCAmelCase__:	Union[str, Any]									= _ask_field("Enter your Docker image: "	,lambda SCREAMING_SNAKE_CASE      : str(_lowerCAmelCase      ).lower()      )

	UpperCAmelCase__:	Dict									= _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."	,)
	UpperCAmelCase__:	Union[str, Any]									= None
	if is_sagemaker_inputs_enabled:
		UpperCAmelCase__:	str									= _ask_field(
		    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "	,lambda SCREAMING_SNAKE_CASE      : str(_lowerCAmelCase      ).lower()	,)

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

	UpperCAmelCase__:	Any									= _ask_options(
	    "What is the distributed mode?"	,["No distributed training", "Data parallelism"]	,_convert_sagemaker_distributed_mode	,)
	UpperCAmelCase__:	int									= {}
	UpperCAmelCase__:	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:
		UpperCAmelCase__:	Union[str, Any]									= "dynamo_"
		UpperCAmelCase__:	str									= _ask_options(
		    "Which dynamo backend would you like to use?"	,[x.lower() for x in DYNAMO_BACKENDS]	,_convert_dynamo_backend	,default=2	,)
		UpperCAmelCase__:	Union[str, Any]									= _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:
			UpperCAmelCase__:	Tuple									= _ask_options(
			    "Which mode do you want to use?"	,_lowerCAmelCase	,lambda SCREAMING_SNAKE_CASE      : TORCH_DYNAMO_MODES[int(_lowerCAmelCase      )]	,default="default"	,)
			UpperCAmelCase__:	Optional[int]									= _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."	,)
			UpperCAmelCase__:	int									= _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."	,)
	UpperCAmelCase__:	Optional[Any]									= "Which EC2 instance type you want to use for your training?"
	if distributed_type != SageMakerDistributedType.NO:
		UpperCAmelCase__:	List[Any]									= _ask_options(
		    _lowerCAmelCase	,_lowerCAmelCase	,lambda SCREAMING_SNAKE_CASE      : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_lowerCAmelCase      )]      )
	else:
		eca_instance_query += "? [ml.p3.2xlarge]:"
		UpperCAmelCase__:	Tuple									= _ask_field(_lowerCAmelCase	,lambda SCREAMING_SNAKE_CASE      : str(_lowerCAmelCase      ).lower()	,default="ml.p3.2xlarge"      )

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

	UpperCAmelCase__:	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=_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 __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
