





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_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  botoa.client('''iam'''			)

				snake_case_						:   Tuple           =  {
				    '''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			)			)
								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=_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 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_						:   Tuple           =  botoa.client('''iam'''			)
				return iam_client.get_role(RoleName=_UpperCamelCase			)["Role"]["Arn"]






def 					lowerCamelCase_						(    )		->						int:






				"""simple docstring"""


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

								snake_case_						:   List[str]           =  _ask_field('''AWS Secret Access Key: '''			)
								snake_case_						:   Any           =  aws_secret_access_key

				snake_case_						:   Optional[Any]           =  _ask_field('''Enter your AWS Region: [us-east-1]'''			,    default='''us-east-1'''			)
				snake_case_						:   List[str]           =  aws_region

				snake_case_						:   List[Any]           =  _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:
								snake_case_						:   Optional[Any]           =  _ask_field('''Enter your IAM role name: '''			)
				else:
								snake_case_						:   Dict           =  '''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			)

				snake_case_						:   Dict           =  _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.'''			,    )
				snake_case_						:   int           =  None
				if is_custom_docker_image:
								snake_case_						:   Union[str, Any]           =  _ask_field('''Enter your Docker image: '''			,    lambda _UpperCamelCase			: str(_UpperCamelCase			).lower()			)

				snake_case_						:   Union[str, Any]           =  _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.'''			,    )
				snake_case_						:   List[Any]           =  None
				if is_sagemaker_inputs_enabled:
								snake_case_						:   str           =  _ask_field(
								    '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''			,    lambda _UpperCamelCase			: str(_UpperCamelCase			).lower()			,    )

				snake_case_						:   Optional[int]           =  _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.'''			,    )
				snake_case_						:   int           =  None
				if is_sagemaker_metrics_enabled:
								snake_case_						:   Optional[int]           =  _ask_field(
								    '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''			,    lambda _UpperCamelCase			: str(_UpperCamelCase			).lower()			,    )

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

				snake_case_						:   Optional[int]           =  1
				if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
								snake_case_						:   str           =  _ask_field(
								    '''How many machines do you want use? [1]: '''			,    _UpperCamelCase			,    default=1			,    )

				snake_case_						:   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=_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			,    )







import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






				"""simple docstring"""


				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case_						:   int           =  n - 1
				snake_case_						:   Union[str, Any]           =  0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case_						:   List[Any]           =  0
				while count < prec:
								snake_case_						:   Tuple           =  random.randint(2			,    n - 1			)
								snake_case_						:   List[Any]           =  bin_exp_mod(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								if b != 1:
												snake_case_						:   Any           =  True
												for _ in range(_UpperCamelCase			):
																if b == n - 1:
																				snake_case_						:   Tuple           =  False
																				break
																snake_case_						:   List[Any]           =  b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
	lowerCAmelCase_    =    abs(int(input('''Enter bound : ''').strip()))
	print('''Here\'s the list of primes:''')
	print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
