


"""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     (      lowerCamelCase_  :      Optional[Any]  ):
				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=lowerCamelCase_						,					AssumeRolePolicyDocument=json.dumps(lowerCamelCase_						,					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=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 __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				botoa.client("iam"  )
				return iam_client.get_role(RoleName=lowerCamelCase_  )["Role"]["Arn"]


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				_ask_options(
				    "How do you want to authorize?"						,					["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,					lowerCamelCase_						,					)
				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"]						,					lowerCamelCase_						,					)
				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(lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				_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."						,					)
				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 lowerCamelCase_  : str(lowerCamelCase_  ).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=lowerCamelCase_						,					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=lowerCamelCase_						,					error_message="Please enter yes or no."						,					)

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




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  ) if (len(lowerCamelCase_  ) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8  )						,					"Stack".center(lowerCamelCase_  )						,					"Postfix".center(lowerCamelCase_  )						,					sep=" | "						,					)
				print("-" * (print_width * 3 + 7)  )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase_  )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase_  )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase_  ) == 0:
																stack.append(lowerCamelCase_  )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase_  ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()  )  # pop stack & add to Postfix
																stack.append(lowerCamelCase_  )  # push x to stack

								print(
								    x.center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				while len(lowerCamelCase_  ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()  )  # pop stack & add to Postfix
								print(
								    " ".center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				return "".join(lowerCamelCase_  )  # return Postfix as str


def __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(infix[::-1]  )  # reverse the infix equation

				for i in range(len(lowerCamelCase_  )  ):
								if infix[i] == "(":
												SCREAMING_SNAKE_CASE__     							=				")"  # change "(" to ")"
								elif infix[i] == ")":
												SCREAMING_SNAKE_CASE__     							=				"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase_  )  ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	_lowerCamelCase     =  input('\nEnter an Infix Equation = ')  # Input an Infix equation
	_lowerCamelCase     =  ''.join(Infix.split())  # Remove spaces from the input
	print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
