
'''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 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Dict      =							botoa.client("""iam"""   )

  a_      :	Optional[Any]      =							{
      """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_      :	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=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 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	List[str]      =							botoa.client("""iam"""   )
  return iam_client.get_role(RoleName=A_   )["Role"]["Arn"]






def 	_snake_case						(      ):
  """simple docstring"""







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

    a_      :	Any      =							_ask_field("""AWS Secret Access Key: """   )
    a_      :	Optional[Any]      =							aws_secret_access_key

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

  a_      :	Dict      =							_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_      :	Union[str, Any]      =							_ask_field("""Enter your IAM role name: """   )
  else:
    a_      :	Dict      =							"""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_      :	Optional[int]      =							_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_      :	List[str]      =							None
  if is_custom_docker_image:
    a_      :	Dict      =							_ask_field("""Enter your Docker image: """     ,		lambda A_   : str(A_   ).lower()   )

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

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

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

  a_      :	Optional[int]      =							1
  if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
    a_      :	List[Any]      =							_ask_field(
        """How many machines do you want use? [1]: """     ,		A_     ,		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=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'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


