





'''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        UpperCamelCase__		(						__magic_name__  :   Optional[Any]      )				->			Dict:
 '''simple docstring'''






 snake_case__     :					int    =  botoa.client("""iam"""      )

 snake_case__     :					Union[str, 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=__magic_name__		,       AssumeRolePolicyDocument=json.dumps(__magic_name__		,       indent=2      )      )
  snake_case__     :					Dict    =  {
      """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=__magic_name__		,       PolicyName=f"{role_name}_policy_permission"		,       PolicyDocument=json.dumps(__magic_name__		,       indent=2      )		,       )
 except iam_client.exceptions.EntityAlreadyExistsException:
  print(f"role {role_name} already exists. Using existing one"      )



def        UpperCamelCase__		(						__magic_name__  :   Any      )				->			Tuple:
 '''simple docstring'''






 snake_case__     :					List[str]    =  botoa.client("""iam"""      )
 return iam_client.get_role(RoleName=__magic_name__      )["Role"]["Arn"]





def        UpperCamelCase__		(						)				->			Tuple:
 '''simple docstring'''






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

  snake_case__     :					Optional[Any]    =  _ask_field("""AWS Secret Access Key: """      )
  snake_case__     :					List[str]    =  aws_secret_access_key

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

 snake_case__     :					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"""]		,       __magic_name__		,       )
 if role_management == 0:
  snake_case__     :					Optional[Any]    =  _ask_field("""Enter your IAM role name: """      )
 else:
  snake_case__     :					Optional[int]    =  """accelerate_sagemaker_execution_role"""
  print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"      )
  _create_iam_role_for_sagemaker(__magic_name__      )

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

 snake_case__     :					Tuple    =  _ask_field(
     """Do you want to provide SageMaker input channels with data locations? [yes/NO]: """		,       _convert_yes_no_to_bool		,       default=__magic_name__		,       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 __magic_name__      : str(__magic_name__      ).lower()		,       )

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

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

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

 snake_case__     :					Dict    =  1
 if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
  snake_case__     :					Optional[Any]    =  _ask_field(
      """How many machines do you want use? [1]: """		,       __magic_name__		,       default=1		,       )

 snake_case__     :					Union[str, 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=__magic_name__		,       compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER		,       distributed_type=__magic_name__		,       use_cpu=__magic_name__		,       dynamo_config=__magic_name__		,       eca_instance_type=__magic_name__		,       profile=__magic_name__		,       region=__magic_name__		,       iam_role_name=__magic_name__		,       mixed_precision=__magic_name__		,       num_machines=__magic_name__		,       sagemaker_inputs_file=__magic_name__		,       sagemaker_metrics_file=__magic_name__		,       )






from math import loga





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if a < 0:
        raise ValueError("Input value must be a positive integer"				)
    elif isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				):
        raise TypeError("Input value must be a 'int' type"				)
    return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



