

# 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      (						A    ):
	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=A       ,     AssumeRolePolicyDocument=json.dumps(A       ,     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=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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			botoa.client("iam"    )
	return iam_client.get_role(RoleName=A    )["Role"]["Arn"]




def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			_ask_options(
	    "How do you want to authorize?"       ,     ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]       ,     A       ,     )
	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"]       ,     A       ,     )
	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(A    )

	UpperCAmelCase_       				=			_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."       ,     )
	UpperCAmelCase_       				=			None
	if is_custom_docker_image:
		UpperCAmelCase_       				=			_ask_field("Enter your Docker image: "       ,     lambda A    : str(A    ).lower()    )

	UpperCAmelCase_       				=			_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."       ,     )
	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 A    : str(A    ).lower()       ,     )

	UpperCAmelCase_       				=			_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."       ,     )
	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 A    : str(A    ).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=A       ,     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=A       ,     error_message="Please enter yes or no."       ,     )

		if use_custom_options:
			UpperCAmelCase_       				=			_ask_options(
			    "Which mode do you want to use?"       ,     A       ,     lambda A    : TORCH_DYNAMO_MODES[int(A    )]       ,     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=A       ,     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=A       ,     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(
		    A       ,     A       ,     lambda A    : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(A    )]    )
	else:
		eca_instance_query += "? [ml.p3.2xlarge]:"
		UpperCAmelCase_       				=			_ask_field(A       ,     lambda A    : str(A    ).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]: "       ,     A       ,     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=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       ,     )


from sklearn.metrics import mean_squared_error

import datasets


_a:    Any 							=			"""\
@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}
}
"""

_a:    List[Any] 							=			"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_a:    List[str] 							=			"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""

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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		return datasets.MetricInfo(
		    description=_DESCRIPTION		,	citation=_CITATION		,	inputs_description=_KWARGS_DESCRIPTION		,	features=datasets.Features(self._get_feature_types()							)		,	reference_urls=[
		        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
		    ]		,	)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		if self.config_name == "multilist":
			return {
			    "predictions": datasets.Sequence(datasets.Value("float"							)							),
			    "references": datasets.Sequence(datasets.Value("float"							)							),
			}
		else:
			return {
			    "predictions": datasets.Value("float"							),
			    "references": datasets.Value("float"							),
			}




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						str="uniform_average"		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			mean_squared_error(
		    lowerCAmelCase		,	lowerCAmelCase		,	sample_weight=lowerCAmelCase		,	multioutput=lowerCAmelCase		,	squared=lowerCAmelCase							)

		return {"mse": mse}