
"""simple docstring"""




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_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     botoa.client("""iam"""		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     {
							    "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=lowercase_     ,AssumeRolePolicyDocument=json.dumps(lowercase_     ,indent=2		)		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     {
														    "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=lowercase_     ,PolicyName=f'''{role_name}_policy_permission'''     ,PolicyDocument=json.dumps(lowercase_     ,indent=2		)     ,)
							except iam_client.exceptions.EntityAlreadyExistsException:
														print(f'''role {role_name} already exists. Using existing one'''		)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     botoa.client("""iam"""		)
							return iam_client.get_role(RoleName=lowercase_		)["Role"]["Arn"]

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

														SCREAMING_SNAKE_CASE__		: Any     =     _ask_field("""AWS Secret Access Key: """		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     aws_secret_access_key

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

							SCREAMING_SNAKE_CASE__		: Optional[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"""]     ,lowercase_     ,)
							if role_management == 0:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     _ask_field("""Enter your IAM role name: """		)
							else:
														SCREAMING_SNAKE_CASE__		: str     =     "accelerate_sagemaker_execution_role"
														print(f'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials'''		)
														_create_iam_role_for_sagemaker(lowercase_		)

							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     _ask_field(
							    """Do you want to use custom Docker image? [yes/NO]: """     ,_convert_yes_no_to_bool     ,default=lowercase_     ,error_message="""Please enter yes or no."""     ,)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     None
							if is_custom_docker_image:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     _ask_field("""Enter your Docker image: """     ,lambda _snake_case		: str(lowercase_		).lower()		)

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

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

							SCREAMING_SNAKE_CASE__		: str     =     _ask_options(
							    """What is the distributed mode?"""     ,["""No distributed training""", """Data parallelism"""]     ,_convert_sagemaker_distributed_mode     ,)
							SCREAMING_SNAKE_CASE__		: str     =     {}
							SCREAMING_SNAKE_CASE__		: List[str]     =     _ask_field(
							    """Do you wish to optimize your script with torch dynamo?[yes/NO]:"""     ,_convert_yes_no_to_bool     ,default=lowercase_     ,error_message="""Please enter yes or no."""     ,)
							if use_dynamo:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     "dynamo_"
														SCREAMING_SNAKE_CASE__		: Tuple     =     _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__		: int     =     _ask_field(
														    """Do you want to customize the defaults sent to torch.compile? [yes/NO]: """     ,_convert_yes_no_to_bool     ,default=lowercase_     ,error_message="""Please enter yes or no."""     ,)

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

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

							SCREAMING_SNAKE_CASE__		: List[Any]     =     _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=lowercase_     ,compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER     ,distributed_type=lowercase_     ,use_cpu=lowercase_     ,dynamo_config=lowercase_     ,eca_instance_type=lowercase_     ,profile=lowercase_     ,region=lowercase_     ,iam_role_name=lowercase_     ,mixed_precision=lowercase_     ,num_machines=lowercase_     ,sagemaker_inputs_file=lowercase_     ,sagemaker_metrics_file=lowercase_     ,)



"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




