






'''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   (							__A							:   int      ):
				a_		:  Tuple											=      botoa.client('''iam'''      )

				a_		:  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=__A						,		AssumeRolePolicyDocument=json.dumps(__A						,		indent=2      )      )
								a_		:  Optional[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=__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 		_UpperCAmelCase   (							__A							:   Tuple      ):
				a_		:  Optional[Any]											=      botoa.client('''iam'''      )
				return iam_client.get_role(RoleName=__A      )["Role"]["Arn"]





def 		_UpperCAmelCase   (							):
				a_		:  Union[str, Any]											=      _ask_options(
				    '''How do you want to authorize?'''						,		['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']						,		__A						,		)
				a_		:  Optional[int]											=      None
				if credentials_configuration == 0:
								a_		:  int											=      _ask_field('''Enter your AWS Profile name: [default] '''						,		default='''default'''      )
								a_		:  int											=      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_		:  List[str]											=      _ask_field('''AWS Access Key ID: '''      )
								a_		:  Optional[int]											=      aws_access_key_id

								a_		:  int											=      _ask_field('''AWS Secret Access Key: '''      )
								a_		:  Optional[int]											=      aws_secret_access_key

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

				a_		:  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''']						,		__A						,		)
				if role_management == 0:
								a_		:  Union[str, Any]											=      _ask_field('''Enter your IAM role name: '''      )
				else:
								a_		:  List[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(__A      )

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

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

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

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

				a_		:  List[Any]											=      1
				if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
								a_		:  Tuple											=      _ask_field(
								    '''How many machines do you want use? [1]: '''						,		__A						,		default=1						,		)

				a_		:  Dict											=      _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						,		)












'''simple docstring'''







from math import pi, sqrt, tan





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''surface_area_cube() only accepts non-negative values'''      )
				return 6 * side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('''surface_area_cuboid() only accepts non-negative values'''      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_sphere() only accepts non-negative values'''      )
				return 4 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''      )
				return 3 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cone() only accepts non-negative values'''      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    '''surface_area_conical_frustum() only accepts non-negative values'''      )
				a_		:  Any											=      (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cylinder() only accepts non-negative values'''      )
				return 2 * pi * radius * (height + radius)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('''surface_area_torus() only accepts non-negative values'''      )
				if torus_radius < tube_radius:
								raise ValueError(
								    '''surface_area_torus() does not support spindle or self intersecting tori'''      )
				return 4 * pow(__A						,		2      ) * torus_radius * tube_radius





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if length < 0 or width < 0:
								raise ValueError('''area_rectangle() only accepts non-negative values'''      )
				return length * width





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''area_square() only accepts non-negative values'''      )
				return side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_triangle() only accepts non-negative values'''      )
				return (base * height) / 2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('''Given three sides do not form a triangle'''      )
				a_		:  int											=      (sidea + sidea + sidea) / 2
				a_		:  Optional[Any]											=      sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_parallelogram() only accepts non-negative values'''      )
				return base * height





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('''area_trapezium() only accepts non-negative values'''      )
				return 1 / 2 * (basea + basea) * height





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''area_circle() only accepts non-negative values'''      )
				return pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius_x < 0 or radius_y < 0:
								raise ValueError('''area_ellipse() only accepts non-negative values'''      )
				return pi * radius_x * radius_y





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('''area_rhombus() only accepts non-negative values'''      )
				return 1 / 2 * diagonal_a * diagonal_a





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   float      ):
				if not isinstance(__A						,		__A      ) or sides < 3:
								raise ValueError(
								    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''      )
				elif length < 0:
								raise ValueError(
								    '''area_reg_polygon() only accepts non-negative values as \
length of a side'''      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('[DEMO] Areas of various geometric shapes: \n')
		print(F"""Rectangle: {area_rectangle(10, 20) = }""")
		print(F"""Square: {area_square(10) = }""")
		print(F"""Triangle: {area_triangle(10, 10) = }""")
		print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(F"""Rhombus: {area_rhombus(10, 20) = }""")
		print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(F"""Circle: {area_circle(20) = }""")
		print(F"""Ellipse: {area_ellipse(10, 20) = }""")
		print('\nSurface Areas of various geometric shapes: \n')
		print(F"""Cube: {surface_area_cube(20) = }""")
		print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(F"""Sphere: {surface_area_sphere(20) = }""")
		print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(F"""Cone: {surface_area_cone(10, 20) = }""")
		print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(F"""Torus: {surface_area_torus(20, 10) = }""")
		print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(F"""Square: {area_reg_polygon(4, 10) = }""")
		print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")




