


"""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			( _SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							snake_case_          =						botoa.client("""iam"""			)

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







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						botoa.client("""iam"""			)
							return iam_client.get_role(RoleName=_SCREAMING_SNAKE_CASE			)["Role"]["Arn"]







def   _a			( )      ->			Any:
							snake_case_          =						_ask_options(
							    """How do you want to authorize?"""			,	["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]			,	_SCREAMING_SNAKE_CASE			,	)
							snake_case_          =						None
							if credentials_configuration == 0:
														snake_case_          =						_ask_field("""Enter your AWS Profile name: [default] """			,	default="""default"""			)
														snake_case_          =						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_          =						_ask_field("""AWS Access Key ID: """			)
														snake_case_          =						aws_access_key_id

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

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

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

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

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

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

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

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

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

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







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							assert column_title.isupper()
							snake_case_          =						0
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			) - 1
							snake_case_          =						0

							while index >= 0:
														snake_case_          =						(ord(column_title[index]			) - 64) * pow(26			,	_SCREAMING_SNAKE_CASE			)
														answer += value
														power += 1
														index -= 1

							return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()



