"""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   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							botoa.client('iam'       )

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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							botoa.client('iam'       )
				return iam_client.get_role(RoleName=lowerCamelCase       )["Role"]["Arn"]







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

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

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

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

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

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

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

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

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

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

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




"""simple docstring"""






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							self.__linear
									elif kernel == "rbf":
													if self.gamma == 0:
																	raise ValueError('rbf kernel requires gamma'			)
													if not isinstance(self.gamma    ,(float, int)			):
																	raise ValueError('gamma must be float or int'			)
													if not self.gamma > 0:
																	raise ValueError('gamma must be > 0'			)
													UpperCAmelCase__             =							self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
									else:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

									def to_minimize(lowerCamelCase__   :	ndarray			) -> float:
													UpperCAmelCase__             =							0
													((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)
													for i in range(lowerCamelCase__			):
																	for j in range(lowerCamelCase__			):
																					s += (
																					    candidate[i]
																					    * candidate[j]
																					    * classes[i]
																					    * classes[j]
																					    * self.kernel(observations[i]    ,observations[j]			)
																					)
													return 1 / 2 * s - sum(lowerCamelCase__			)

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

									# calculating mean offset of separation plane to points
									UpperCAmelCase__             =							0
									for i in range(lowerCamelCase__			):
													for j in range(lowerCamelCase__			):
																	s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																	    observations[i]    ,observations[j]			)
									UpperCAmelCase__             =							s / n







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



