


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		(		_lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			botoa.client('iam' )

     __UpperCamelCase =			{
         """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=_lowercase					,  AssumeRolePolicyDocument=json.dumps(_lowercase					,  indent=2 ) )
          __UpperCamelCase =			{
              """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=_lowercase					,  PolicyName=f'''{role_name}_policy_permission'''					,  PolicyDocument=json.dumps(_lowercase					,  indent=2 )					,  )
     except iam_client.exceptions.EntityAlreadyExistsException:
          print(f'''role {role_name} already exists. Using existing one''' )






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			botoa.client('iam' )
     return iam_client.get_role(RoleName=_lowercase )["Role"]["Arn"]






def 		_A		(		)					-> Any:


     """simple docstring"""


     __UpperCamelCase =			_ask_options(
         'How do you want to authorize?'					,  ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']					,  _lowercase					,  )
     __UpperCamelCase =			None
     if credentials_configuration == 0:
          __UpperCamelCase =			_ask_field('Enter your AWS Profile name: [default] '					,  default='default' )
          __UpperCamelCase =			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 =			_ask_field('AWS Access Key ID: ' )
          __UpperCamelCase =			aws_access_key_id

          __UpperCamelCase =			_ask_field('AWS Secret Access Key: ' )
          __UpperCamelCase =			aws_secret_access_key

     __UpperCamelCase =			_ask_field('Enter your AWS Region: [us-east-1]'					,  default='us-east-1' )
     __UpperCamelCase =			aws_region

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

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

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

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

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

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

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

     __UpperCamelCase =			_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=_lowercase					,  compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER					,  distributed_type=_lowercase					,  use_cpu=_lowercase					,  dynamo_config=_lowercase					,  eca_instance_type=_lowercase					,  profile=_lowercase					,  region=_lowercase					,  iam_role_name=_lowercase					,  mixed_precision=_lowercase					,  num_machines=_lowercase					,  sagemaker_inputs_file=_lowercase					,  sagemaker_metrics_file=_lowercase					,  )









"""simple docstring"""


from math import ceil, sqrt

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 100_0000     ):
						UpperCamelCase				:		int				   =			0

						for outer_width in range(3							,     (limit // 4) + 2     ):
												if outer_width**2 > limit:
																		UpperCamelCase				:		Optional[Any]				   =			max(ceil(sqrt(outer_width**2 - limit     )     )							,     1     )
												else:
																		UpperCamelCase				:		str				   =			1
												if (outer_width - hole_width_lower_bound) % 2:
																		hole_width_lower_bound += 1

												answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

						return answer


if __name__ == "__main__":
		print(f'''{solution() = }''')



