





"""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 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       botoa.client("iam"			)

      _UpperCamelCase      :	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=lowercase_		,AssumeRolePolicyDocument=json.dumps(lowercase_		,indent=2			)			)
            _UpperCamelCase      :	List[str]						=       {
                "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=lowercase_		,PolicyName=F'''{role_name}_policy_permission'''		,PolicyDocument=json.dumps(lowercase_		,indent=2			)		,)
      except iam_client.exceptions.EntityAlreadyExistsException:
            print(F'''role {role_name} already exists. Using existing one'''			)




def 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       botoa.client("iam"			)
      return iam_client.get_role(RoleName=lowercase_			)["Role"]["Arn"]




def 					lowercase__    (					)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	str						=       _ask_options(
          "How do you want to authorize?"		,["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]		,lowercase_		,)
      _UpperCamelCase      :	List[Any]						=       None
      if credentials_configuration == 0:
            _UpperCamelCase      :	int						=       _ask_field("Enter your AWS Profile name: [default] "		,default="default"			)
            _UpperCamelCase      :	Optional[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`"			)
            _UpperCamelCase      :	Union[str, Any]						=       _ask_field("AWS Access Key ID: "			)
            _UpperCamelCase      :	Any						=       aws_access_key_id

            _UpperCamelCase      :	Any						=       _ask_field("AWS Secret Access Key: "			)
            _UpperCamelCase      :	Tuple						=       aws_secret_access_key

      _UpperCamelCase      :	Union[str, Any]						=       _ask_field("Enter your AWS Region: [us-east-1]"		,default="us-east-1"			)
      _UpperCamelCase      :	str						=       aws_region

      _UpperCamelCase      :	Optional[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"]		,lowercase_		,)
      if role_management == 0:
            _UpperCamelCase      :	List[Any]						=       _ask_field("Enter your IAM role name: "			)
      else:
            _UpperCamelCase      :	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(lowercase_			)

      _UpperCamelCase      :	Union[str, Any]						=       _ask_field(
          "Do you want to use custom Docker image? [yes/NO]: "		,_convert_yes_no_to_bool		,default=lowercase_		,error_message="Please enter yes or no."		,)
      _UpperCamelCase      :	Optional[int]						=       None
      if is_custom_docker_image:
            _UpperCamelCase      :	Optional[int]						=       _ask_field("Enter your Docker image: "		,lambda lowercase_			: str(lowercase_			).lower()			)

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

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

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

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

      _UpperCamelCase      :	Optional[int]						=       1
      if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
            _UpperCamelCase      :	Any						=       _ask_field(
                "How many machines do you want use? [1]: "		,lowercase_		,default=1		,)

      _UpperCamelCase      :	List[Any]						=       _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=lowercase_		,compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER		,distributed_type=lowercase_		,use_cpu=lowercase_		,dynamo_config=lowercase_		,eca_instance_type=lowercase_		,profile=lowercase_		,region=lowercase_		,iam_role_name=lowercase_		,mixed_precision=lowercase_		,num_machines=lowercase_		,sagemaker_inputs_file=lowercase_		,sagemaker_metrics_file=lowercase_		,)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
