
# 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 		UpperCAmelCase     (      a_		)						->      Optional[int]:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      a_		)						->      Optional[int]:







				"""simple docstring"""



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


def 		UpperCAmelCase     (      )						->      Optional[int]:







				"""simple docstring"""



				__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_						,  )

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)







@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     list_field(
							    default=[],  metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    },  )

							UpperCAmelCase				   =     list_field(
							    default=[8],  metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )

							UpperCAmelCase				   =     list_field(
							    default=[8, 32, 128, 512],  metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},  )

							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Use FP16 to accelerate inference."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Benchmark training of model"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Verbose memory tracing"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    },  )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Trace memory line by line"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save result to a CSV file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save all print statements in a log file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Whether to print environment information"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""env_info_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving environment information."},  )
							UpperCAmelCase				   =     field(
							    default=F"""log_{round(time() )}.csv""",  metadata={"help": "Log filename used if print statements are saved in log."},  )
							UpperCAmelCase				   =     field(default=3,  metadata={"help": "Times an experiment will be run."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    },  )







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												warnings.warn(
												    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
												    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
												    ''' to benchmark Transformer models.''' ,			_A ,			)







							def  UpperCamelCase_						(   self      :     str		):
												return json.dumps(dataclasses.asdict(self		) ,			indent=2		)







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												if len(self.models		) <= 0:
																	raise ValueError(
																	    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																	    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''		)
												return self.models







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												if not self.multi_process:
																	return False
												elif self.is_tpu:
																	logger.info('''Multiprocessing is currently not possible on TPU.'''		)
																	return False
												else:
																	return True
