



"""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       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Union[str, Any]				= botoa.client("iam"			)

			__lowerCAmelCase   :						Dict				= {
			    "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=__snake_case      ,AssumeRolePolicyDocument=json.dumps(__snake_case      ,indent=2			)			)
						__lowerCAmelCase   :						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=__snake_case      ,PolicyName=F"""{role_name}_policy_permission"""      ,PolicyDocument=json.dumps(__snake_case      ,indent=2			)      ,)
			except iam_client.exceptions.EntityAlreadyExistsException:
						print(F"""role {role_name} already exists. Using existing one"""			)


def     _lowercase       (						__snake_case			)		->     str:
			__lowerCAmelCase   :						Optional[Any]				= botoa.client("iam"			)
			return iam_client.get_role(RoleName=__snake_case			)["Role"]["Arn"]





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

						__lowerCAmelCase   :						int				= _ask_field("AWS Secret Access Key: "			)
						__lowerCAmelCase   :						Tuple				= aws_secret_access_key

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

			__lowerCAmelCase   :						Union[str, 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"]      ,__snake_case      ,)
			if role_management == 0:
						__lowerCAmelCase   :						Union[str, Any]				= _ask_field("Enter your IAM role name: "			)
			else:
						__lowerCAmelCase   :						Optional[Any]				= "accelerate_sagemaker_execution_role"
						print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""			)
						_create_iam_role_for_sagemaker(__snake_case			)

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

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

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

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

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

			__lowerCAmelCase   :						List[Any]				= 1
			if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
						__lowerCAmelCase   :						List[Any]				= _ask_field(
						    "How many machines do you want use? [1]: "      ,__snake_case      ,default=1      ,)

			__lowerCAmelCase   :						List[str]				= _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=__snake_case      ,compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER      ,distributed_type=__snake_case      ,use_cpu=__snake_case      ,dynamo_config=__snake_case      ,eca_instance_type=__snake_case      ,profile=__snake_case      ,region=__snake_case      ,iam_role_name=__snake_case      ,mixed_precision=__snake_case      ,num_machines=__snake_case      ,sagemaker_inputs_file=__snake_case      ,sagemaker_metrics_file=__snake_case      ,)




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)