'''simple docstring'''



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  (			lowerCamelCase_     :					Dict			):
			__lowercase			  =							botoa.client('''iam'''			)

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]			):
			__lowercase			  =							botoa.client('''iam'''			)
			return iam_client.get_role(RoleName=__snake_case			)["Role"]["Arn"]




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

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

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

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

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

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

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

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

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

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

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












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4





