
'''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 (__lowerCamelCase :int						):
						_lowerCAmelCase    		=					botoa.client("""iam"""						)

						_lowerCAmelCase    		=					{
						    """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						)						)
												_lowerCAmelCase    		=					{
												    """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 (__lowerCamelCase :List[str]						):
						_lowerCAmelCase    		=					botoa.client("""iam"""						)
						return iam_client.get_role(RoleName=__lowerCamelCase						)["Role"]["Arn"]





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

												_lowerCAmelCase    		=					_ask_field("""AWS Secret Access Key: """						)
												_lowerCAmelCase    		=					aws_secret_access_key

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

						_lowerCAmelCase    		=					_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:
												_lowerCAmelCase    		=					_ask_field("""Enter your IAM role name: """						)
						else:
												_lowerCAmelCase    		=					"""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						)

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

						_lowerCAmelCase    		=					_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."""					,      )
						_lowerCAmelCase    		=					None
						if is_sagemaker_inputs_enabled:
												_lowerCAmelCase    		=					_ask_field(
												    """Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """					,      lambda __lowerCamelCase						: str(__lowerCamelCase						).lower()					,      )

						_lowerCAmelCase    		=					_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."""					,      )
						_lowerCAmelCase    		=					None
						if is_sagemaker_metrics_enabled:
												_lowerCAmelCase    		=					_ask_field(
												    """Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """					,      lambda __lowerCamelCase						: str(__lowerCamelCase						).lower()					,      )

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

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

						_lowerCAmelCase    		=					_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'''



def A ():
						for n in range(1					,      1000000						):
												yield n * (n + 1) // 2


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					2
						while i * i <= n:
												_lowerCAmelCase    		=					0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count


def A ():
						return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase						) > 500						)


if __name__ == "__main__":
						print(solution())
