


# 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      __lowerCamelCase		(				lowerCamelCase__						:		Dict	):





			'''simple docstring'''




			lowerCamelCase  						=   botoa.client("""iam"""	)

			lowerCamelCase  						=   {
			    '''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=UpperCAmelCase_   ,				AssumeRolePolicyDocument=json.dumps(UpperCAmelCase_   ,				indent=2	)	)
						lowerCamelCase  						=   {
						    '''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=UpperCAmelCase_   ,				PolicyName=f'{role_name}_policy_permission'   ,				PolicyDocument=json.dumps(UpperCAmelCase_   ,				indent=2	)   ,				)
			except iam_client.exceptions.EntityAlreadyExistsException:
						print(f'role {role_name} already exists. Using existing one'	)






def      __lowerCamelCase		(				lowerCamelCase__						:		Optional[int]	):





			'''simple docstring'''




			lowerCamelCase  						=   botoa.client("""iam"""	)
			return iam_client.get_role(RoleName=UpperCAmelCase_	)["Role"]["Arn"]






def      __lowerCamelCase		(				):





			'''simple docstring'''




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

						lowerCamelCase  						=   _ask_field("""AWS Secret Access Key: """	)
						lowerCamelCase  						=   aws_secret_access_key

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

			lowerCamelCase  						=   _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"""]   ,				UpperCAmelCase_   ,				)
			if role_management == 0:
						lowerCamelCase  						=   _ask_field("""Enter your IAM role name: """	)
			else:
						lowerCamelCase  						=   '''accelerate_sagemaker_execution_role'''
						print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials'	)
						_create_iam_role_for_sagemaker(UpperCAmelCase_	)

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

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

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

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

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

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

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





# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case				:    int       				=     '''Create a default config file for Accelerate with only a few flags set.'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any]="no" ,  UpperCAmelCase_						:		str = default_json_config_file ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[str] 							=       Path(UpperCAmelCase_						)
      path.parent.mkdir(parents=UpperCAmelCase_ ,  exist_ok=UpperCAmelCase_						)
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''						)
            return False
      a						:Optional[Any] 							=       mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''						)
      a						:List[Any] 							=       {
          '''compute_environment''': '''LOCAL_MACHINE''',
          '''mixed_precision''': mixed_precision,
      }
      if torch.cuda.is_available():
            a						:Dict 							=       torch.cuda.device_count()
            a						:Tuple 							=       num_gpus
            a						:int 							=       False
            if num_gpus > 1:
                  a						:str 							=       '''MULTI_GPU'''
            else:
                  a						:List[Any] 							=       '''NO'''
      elif is_xpu_available() and use_xpu:
            a						:List[Any] 							=       torch.xpu.device_count()
            a						:Optional[int] 							=       num_xpus
            a						:List[Any] 							=       False
            if num_xpus > 1:
                  a						:int 							=       '''MULTI_XPU'''
            else:
                  a						:str 							=       '''NO'''
      elif is_npu_available():
            a						:List[str] 							=       torch.npu.device_count()
            a						:Any 							=       num_npus
            a						:Optional[int] 							=       False
            if num_npus > 1:
                  a						:List[str] 							=       '''MULTI_NPU'''
            else:
                  a						:Dict 							=       '''NO'''
      else:
            a						:str 							=       0
            a						:Optional[Any] 							=       True
            a						:Optional[Any] 							=       1
            a						:str 							=       '''NO'''
      a						:List[str] 							=       ClusterConfig(**UpperCAmelCase_						)
      config.to_json_file(UpperCAmelCase_						)
      return path
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:List[Any] 							=       parser.add_parser('''default''' ,  parents=UpperCAmelCase_ ,  help=UpperCAmelCase_ ,  formatter_class=UpperCAmelCase_						)
      parser.add_argument(
          '''--config_file''' ,  default=UpperCAmelCase_ ,  help=(
              '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          ) ,  dest='''save_location''' ,  )

      parser.add_argument(
          '''--mixed_precision''' ,  choices=['''no''', '''fp16''', '''bf16'''] ,  type=UpperCAmelCase_ ,  help='''Whether or not to use mixed precision training. '''
          '''Choose between FP16 and BF16 (bfloat16) training. '''
          '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,  default='''no''' ,  )
      parser.set_defaults(func=UpperCAmelCase_						)
      return parser






def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       write_basic_config(args.mixed_precision ,  args.save_location						)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''						)





