# 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						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	botoa.client('''iam'''			)

    __UpperCamelCase  :Tuple    						=	{
        '''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			)			)
        __UpperCamelCase  :Tuple    						=	{
            '''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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	botoa.client('''iam'''			)
    return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE			)["Role"]["Arn"]



def lowerCamelCase						(     ):
    '''simple docstring'''







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

        __UpperCamelCase  :List[Any]    						=	_ask_field('''AWS Secret Access Key: '''			)
        __UpperCamelCase  :Optional[Any]    						=	aws_secret_access_key

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

    __UpperCamelCase  :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''']						,    SCREAMING_SNAKE_CASE						,    )
    if role_management == 0:
        __UpperCamelCase  :Optional[int]    						=	_ask_field('''Enter your IAM role name: '''			)
    else:
        __UpperCamelCase  :Dict    						=	'''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			)

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

    __UpperCamelCase  :List[str]    						=	_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.'''						,    )
    __UpperCamelCase  :Union[str, Any]    						=	None
    if is_sagemaker_inputs_enabled:
        __UpperCamelCase  :Union[str, Any]    						=	_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()						,    )

    __UpperCamelCase  :List[Any]    						=	_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.'''						,    )
    __UpperCamelCase  :Any    						=	None
    if is_sagemaker_metrics_enabled:
        __UpperCamelCase  :Union[str, Any]    						=	_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()						,    )

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

    __UpperCamelCase  :Union[str, Any]    						=	1
    if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
        __UpperCamelCase  :Any    						=	_ask_field(
            '''How many machines do you want use? [1]: '''						,    SCREAMING_SNAKE_CASE						,    default=1						,    )

    __UpperCamelCase  :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=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						,    )






from __future__ import annotations

import bisect



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :str    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[int]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  :List[Any]    						=	mid + 1
        else:
            __UpperCamelCase  :Any    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[Any]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  :Dict    						=	mid + 1
        else:
            __UpperCamelCase  :Optional[Any]    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			) - 1

    while left <= right:
        __UpperCamelCase  :Optional[int]    						=	left + (right - left) // 2
        __UpperCamelCase  :List[str]    						=	sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  :Union[str, Any]    						=	midpoint - 1
        else:
            __UpperCamelCase  :Dict    						=	midpoint + 1
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bisect.bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    if index != len(SCREAMING_SNAKE_CASE			) and sorted_collection[index] == item:
        return index
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if right < left:
        return None

    __UpperCamelCase  :str    						=	left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint - 1			)
    else:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint + 1						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      input('''Enter numbers separated by comma:\n''').strip()
    __lowercase											=      sorted(int(item) for item in user_input.split(''','''))
    __lowercase											=      int(input('''Enter a single number to be found in the list:\n'''))
    __lowercase											=      binary_search(collection, target)
    if result is None:
        print(F'{target} was not found in {collection}.')
    else:
        print(F'{target} was found at position {result} in {collection}.')





