





# 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       _A  (      lowerCamelCase   ):
   a__	:						Union[str, Any]      		=  botoa.client("iam"   )

   a__	:						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=lowerCamelCase							, AssumeRolePolicyDocument=json.dumps(lowerCamelCase							, indent=2   )   )
      a__	:						Optional[Any]      		=  {
          "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=lowerCamelCase							, PolicyName=F"""{role_name}_policy_permission"""							, PolicyDocument=json.dumps(lowerCamelCase							, indent=2   )							, )
   except iam_client.exceptions.EntityAlreadyExistsException:
      print(F"""role {role_name} already exists. Using existing one"""   )






def       _A  (      lowerCamelCase   ):
   a__	:						Union[str, Any]      		=  botoa.client("iam"   )
   return iam_client.get_role(RoleName=lowerCamelCase   )["Role"]["Arn"]






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

      a__	:						List[str]      		=  _ask_field("AWS Secret Access Key: "   )
      a__	:						Optional[Any]      		=  aws_secret_access_key

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

   a__	:						Tuple      		=  _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"]							, lowerCamelCase							, )
   if role_management == 0:
      a__	:						Any      		=  _ask_field("Enter your IAM role name: "   )
   else:
      a__	:						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(lowerCamelCase   )

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

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

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

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

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

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

   a__	:						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=lowerCamelCase							, compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER							, distributed_type=lowerCamelCase							, use_cpu=lowerCamelCase							, dynamo_config=lowerCamelCase							, eca_instance_type=lowerCamelCase							, profile=lowerCamelCase							, region=lowerCamelCase							, iam_role_name=lowerCamelCase							, mixed_precision=lowerCamelCase							, num_machines=lowerCamelCase							, sagemaker_inputs_file=lowerCamelCase							, sagemaker_metrics_file=lowerCamelCase							, )








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

