




'''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 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					botoa.client('iam'			)

  __a			=					{
      '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			)			)
    __a			=					{
        '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 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]			):
  __a			=					botoa.client('iam'			)
  return iam_client.get_role(RoleName=a_			)["Role"]["Arn"]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					_ask_options(
      'How do you want to authorize?'				,					['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']				,					a_				,					)
  __a			=					None
  if credentials_configuration == 0:
    __a			=					_ask_field('Enter your AWS Profile name: [default] '				,					default='default'			)
    __a			=					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			=					_ask_field('AWS Access Key ID: '			)
    __a			=					aws_access_key_id

    __a			=					_ask_field('AWS Secret Access Key: '			)
    __a			=					aws_secret_access_key

  __a			=					_ask_field('Enter your AWS Region: [us-east-1]'				,					default='us-east-1'			)
  __a			=					aws_region

  __a			=					_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:
    __a			=					_ask_field('Enter your IAM role name: '			)
  else:
    __a			=					'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_			)

  __a			=					_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.'				,					)
  __a			=					None
  if is_custom_docker_image:
    __a			=					_ask_field('Enter your Docker image: '				,					lambda a_			: str(a_			).lower()			)

  __a			=					_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.'				,					)
  __a			=					None
  if is_sagemaker_inputs_enabled:
    __a			=					_ask_field(
        'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '				,					lambda a_			: str(a_			).lower()				,					)

  __a			=					_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.'				,					)
  __a			=					None
  if is_sagemaker_metrics_enabled:
    __a			=					_ask_field(
        'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '				,					lambda a_			: str(a_			).lower()				,					)

  __a			=					_ask_options(
      'What is the distributed mode?'				,					['No distributed training', 'Data parallelism']				,					_convert_sagemaker_distributed_mode				,					)
  __a			=					{}
  __a			=					_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:
    __a			=					'dynamo_'
    __a			=					_ask_options(
        'Which dynamo backend would you like to use?'				,					[x.lower() for x in DYNAMO_BACKENDS]				,					_convert_dynamo_backend				,					default=2				,					)
    __a			=					_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:
      __a			=					_ask_options(
          'Which mode do you want to use?'				,					a_				,					lambda a_			: TORCH_DYNAMO_MODES[int(a_			)]				,					default='default'				,					)
      __a			=					_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.'				,					)
      __a			=					_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.'				,					)
  __a			=					'Which EC2 instance type you want to use for your training?'
  if distributed_type != SageMakerDistributedType.NO:
    __a			=					_ask_options(
        a_				,					a_				,					lambda a_			: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(a_			)]			)
  else:
    eca_instance_query += "? [ml.p3.2xlarge]:"
    __a			=					_ask_field(a_				,					lambda a_			: str(a_			).lower()				,					default='ml.p3.2xlarge'			)

  __a			=					1
  if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
    __a			=					_ask_field(
        'How many machines do you want use? [1]: '				,					a_				,					default=1				,					)

  __a			=					_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_				,					)









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



