
# 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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       int:

				"""simple docstring"""







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

				A__  :	str         =							{
				    '''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       )       )
								A__  :	str         =							{
								    '''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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       List[str]:

				"""simple docstring"""







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



def 				SCREAMING_SNAKE_CASE     (      ) ->       int:

				"""simple docstring"""







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

								A__  :	str         =							_ask_field('''AWS Secret Access Key: '''       )
								A__  :	Optional[Any]         =							aws_secret_access_key

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

				A__  :	List[str]         =							_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:
								A__  :	Tuple         =							_ask_field('''Enter your IAM role name: '''       )
				else:
								A__  :	str         =							'''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       )

				A__  :	List[str]         =							_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.'''							,						)
				A__  :	str         =							None
				if is_custom_docker_image:
								A__  :	Union[str, Any]         =							_ask_field('''Enter your Docker image: '''							,						lambda __UpperCamelCase       : str(__UpperCamelCase       ).lower()       )

				A__  :	str         =							_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.'''							,						)
				A__  :	Optional[Any]         =							None
				if is_sagemaker_inputs_enabled:
								A__  :	Dict         =							_ask_field(
								    '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''							,						lambda __UpperCamelCase       : str(__UpperCamelCase       ).lower()							,						)

				A__  :	Dict         =							_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.'''							,						)
				A__  :	int         =							None
				if is_sagemaker_metrics_enabled:
								A__  :	Any         =							_ask_field(
								    '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''							,						lambda __UpperCamelCase       : str(__UpperCamelCase       ).lower()							,						)

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

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

				A__  :	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=__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							,						)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				if n == 1 or not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								return 0
				elif n == 2:
								return 1
				else:
								A__  :	Any         =							[0, 1]
								for i in range(2							,						n + 1       ):
												sequence.append(sequence[i - 1] + sequence[i - 2]       )

								return sequence[n]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				A__  :	Dict         =							0
				A__  :	Optional[int]         =							2

				while digits < n:
								index += 1
								A__  :	Dict         =							len(str(fibonacci(__UpperCamelCase       )       )       )

				return index

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int = 10_00       ) ->       int:

				"""simple docstring"""







				return fibonacci_digits_index(__UpperCamelCase       )


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))