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

 lowerCamelCase		:	Optional[int]							=      {
     """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							)							)
  lowerCamelCase		:	Tuple							=      {
      """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							):
 lowerCamelCase		:	Union[str, Any]							=      botoa.client("""iam"""							)
 return iam_client.get_role(RoleName=lowerCamelCase							)["Role"]["Arn"]





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

  lowerCamelCase		:	Tuple							=      _ask_field("""AWS Secret Access Key: """							)
  lowerCamelCase		:	Optional[int]							=      aws_secret_access_key

 lowerCamelCase		:	Any							=      _ask_field("""Enter your AWS Region: [us-east-1]""",	default="""us-east-1"""							)
 lowerCamelCase		:	int							=      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"""],	lowerCamelCase,	)
 if role_management == 0:
  lowerCamelCase		:	Any							=      _ask_field("""Enter your IAM role name: """							)
 else:
  lowerCamelCase		:	List[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							)

 lowerCamelCase		:	Tuple							=      _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.""",	)
 lowerCamelCase		:	int							=      None
 if is_custom_docker_image:
  lowerCamelCase		:	List[str]							=      _ask_field("""Enter your Docker image: """,	lambda lowerCamelCase							: str(lowerCamelCase							).lower()							)

 lowerCamelCase		:	Dict							=      _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.""",	)
 lowerCamelCase		:	Union[str, Any]							=      None
 if is_sagemaker_inputs_enabled:
  lowerCamelCase		:	Dict							=      _ask_field(
      """Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """,	lambda lowerCamelCase							: str(lowerCamelCase							).lower(),	)

 lowerCamelCase		:	int							=      _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.""",	)
 lowerCamelCase		:	Optional[int]							=      None
 if is_sagemaker_metrics_enabled:
  lowerCamelCase		:	List[str]							=      _ask_field(
      """Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """,	lambda lowerCamelCase							: str(lowerCamelCase							).lower(),	)

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

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






def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




