
# 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     __lowerCAmelCase			(			a__  )       ->  Dict:
				__a										=    botoa.client('''iam'''  )

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


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				__a										=    botoa.client('''iam'''  )
				return iam_client.get_role(RoleName=a__  )["Role"]["Arn"]






def     __lowerCAmelCase			(			)       ->  Tuple:
				__a										=    _ask_options(
				    '''How do you want to authorize?'''						,    ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']						,    a__						,    )
				__a										=    None
				if credentials_configuration == 0:
								__a										=    _ask_field('''Enter your AWS Profile name: [default] '''						,    default='''default'''  )
								__a										=    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										=    _ask_field('''AWS Access Key ID: '''  )
								__a										=    aws_access_key_id

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

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

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

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

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

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

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

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

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

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

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()