

'''simple docstring'''
import numpy as np

import datasets


lowerCamelCase :      str					 =     "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowerCamelCase :      Union[str, Any]					 =     "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowerCamelCase :      str					 =     "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,   _KWARGS_DESCRIPTION  )
class 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Union[str, Any] )	->    Union[str, Any]:





       '''simple docstring'''






       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'X': datasets.Sequence(datasets.Value('float'							,					id='sequence' )							,					id='X' ),
               } )							,					)





  def 						A  (		self      :	Any							,					_a      :	Tuple							,					_a      :	Tuple )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =np.array(_a )
       _SCREAMING_SNAKE_CASE							    =np.array(_a )

       # Assert that arrays are 2D
       if len(X.shape ) != 2:
            raise ValueError('Expected `X` to be a 2D vector' )
       if len(reference_distribution.shape ) != 2:
            raise ValueError('Expected `reference_distribution` to be a 2D vector' )
       if reference_distribution.shape[0] < 2:
            raise ValueError(
                'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension' )

       # Get mahalanobis distance for each prediction
       _SCREAMING_SNAKE_CASE							    =X - np.mean(_a )
       _SCREAMING_SNAKE_CASE							    =np.cov(reference_distribution.T )
       try:
            _SCREAMING_SNAKE_CASE							    =np.linalg.inv(_a )
       except np.linalg.LinAlgError:
            _SCREAMING_SNAKE_CASE							    =np.linalg.pinv(_a )
       _SCREAMING_SNAKE_CASE							    =np.dot(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =np.dot(_a							,					X_minus_mu.T ).diagonal()

       return {"mahalanobis": mahal_dist}



'''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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =botoa.client('iam'				)

     _SCREAMING_SNAKE_CASE							    ={
         '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				)				)
          _SCREAMING_SNAKE_CASE							    ={
              '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        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =botoa.client('iam'				)
     return iam_client.get_role(RoleName=_UpperCamelCase				)["Role"]["Arn"]







def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




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

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

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

     _SCREAMING_SNAKE_CASE							    =_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:
          _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your IAM role name: '				)
     else:
          _SCREAMING_SNAKE_CASE							    ='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				)

     _SCREAMING_SNAKE_CASE							    =_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.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_custom_docker_image:
          _SCREAMING_SNAKE_CASE							    =_ask_field('Enter your Docker image: '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()				)

     _SCREAMING_SNAKE_CASE							    =_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.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_sagemaker_inputs_enabled:
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()							, )

     _SCREAMING_SNAKE_CASE							    =_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.'							, )
     _SCREAMING_SNAKE_CASE							    =None
     if is_sagemaker_metrics_enabled:
          _SCREAMING_SNAKE_CASE							    =_ask_field(
              'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '							, lambda _UpperCamelCase				: str(_UpperCamelCase				).lower()							, )

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

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

     _SCREAMING_SNAKE_CASE							    =_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							, )
