




"""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      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       botoa.client('''iam'''	)

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







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

						__lowercase			:  int								=       _ask_field('''AWS Secret Access Key: '''	)
						__lowercase			:  List[str]								=       aws_secret_access_key

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

			__lowercase			:  List[Any]								=       _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:
						__lowercase			:  List[Any]								=       _ask_field('''Enter your IAM role name: '''	)
			else:
						__lowercase			:  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(__UpperCamelCase	)

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

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

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

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

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

			__lowercase			:  List[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	,				)










from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__           =			10





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							for i in range(a__ ,      a__							):
														if array[i] == target:
																					return i
							return -1





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      len(a__							)
							while left <= right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)

														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					UpperCamelCase__					  =      one_third - 1
														elif array[two_third] < target:
																					UpperCamelCase__					  =      two_third + 1

														else:
																					UpperCamelCase__					  =      one_third + 1
																					UpperCamelCase__					  =      two_third - 1
							else:
														return -1





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							if left < right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)
														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(a__ ,      one_third - 1 ,      a__ ,      a__							)
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1 ,      a__ ,      a__ ,      a__							)
														else:
																					return rec_ternary_search(one_third + 1 ,      two_third - 1 ,      a__ ,      a__							)
							else:
														return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase__           =			input("Enter numbers separated by comma:\n").strip()
					UpperCamelCase__           =			[int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					UpperCamelCase__           =			int(input("Enter the number to be found in the list:\n").strip())
					UpperCamelCase__           =			ite_ternary_search(collection, target)
					UpperCamelCase__           =			rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f"""Iterative search: {target} found at positions: {resulta}""")
										print(f"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print("Not found")




