





'''simple docstring'''

from sklearn.metrics import matthews_corrcoef

import datasets


lowercase_					= """
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

lowercase_					= """
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

lowercase_					= """\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""


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

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Any:
        return datasets.MetricInfo(
            description=_DESCRIPTION ,		citation=_CITATION ,		inputs_description=_KWARGS_DESCRIPTION ,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""int32"""       ),
                    """references""": datasets.Value("""int32"""       ),
                }       ) ,		reference_urls=[
                """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
            ] ,		)





    def 			snake_case_(		self ,		A ,		A ,		A=None       )		->   Tuple:
        return {
            "matthews_correlation": float(matthews_corrcoef(A ,		A ,		sample_weight=A       )       ),
        }






'''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							, )
