

'''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 		UpperCamelCase			(					a					) ->      Dict:






     '''simple docstring'''
     __magic_name__		  =							botoa.client('''iam'''					)

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




def 		UpperCamelCase			(					a					) ->      Optional[int]:






     '''simple docstring'''
     __magic_name__		  =							botoa.client('''iam'''					)
     return iam_client.get_role(RoleName=__SCREAMING_SNAKE_CASE					)["Role"]["Arn"]




def 		UpperCamelCase			(					) ->      Tuple:






     '''simple docstring'''
     __magic_name__		  =							_ask_options(
         '''How do you want to authorize?'''						,		['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']						,		__SCREAMING_SNAKE_CASE						,		)
     __magic_name__		  =							None
     if credentials_configuration == 0:
          __magic_name__		  =							_ask_field('''Enter your AWS Profile name: [default] '''						,		default='''default'''					)
          __magic_name__		  =							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`'''					)
          __magic_name__		  =							_ask_field('''AWS Access Key ID: '''					)
          __magic_name__		  =							aws_access_key_id

          __magic_name__		  =							_ask_field('''AWS Secret Access Key: '''					)
          __magic_name__		  =							aws_secret_access_key

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

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

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

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

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

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

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

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

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












'''simple docstring'''






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A_			      =		16
A_			      =		32






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = 16		)    ->   int:
      snake_case__       :   Tuple	      =    AutoTokenizer.from_pretrained('bert-base-cased'		)
      snake_case__       :   Tuple	      =    load_dataset('glue'				,      'mrpc'		)

      def tokenize_function(__SCREAMING_SNAKE_CASE		):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__       :   int	      =    tokenizer(examples['sentence1']				,      examples['sentence2']				,      truncation=__SCREAMING_SNAKE_CASE				,      max_length=__SCREAMING_SNAKE_CASE		)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            snake_case__       :   Union[str, Any]	      =    datasets.map(
                __SCREAMING_SNAKE_CASE				,      batched=__SCREAMING_SNAKE_CASE				,      remove_columns=['idx', 'sentence1', 'sentence2']				,      )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      snake_case__       :   Optional[int]	      =    tokenized_datasets.rename_column('label'				,      'labels'		)

      def collate_fn(__SCREAMING_SNAKE_CASE		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case__       :   Optional[int]	      =    128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case__       :   int	      =    16
            elif accelerator.mixed_precision != "no":
                  snake_case__       :   Tuple	      =    8
            else:
                  snake_case__       :   Tuple	      =    None

            return tokenizer.pad(
                __SCREAMING_SNAKE_CASE				,      padding='longest'				,      max_length=__SCREAMING_SNAKE_CASE				,      pad_to_multiple_of=__SCREAMING_SNAKE_CASE				,      return_tensors='pt'				,      )

      # Instantiate dataloaders.
      snake_case__       :   Any	      =    DataLoader(
          tokenized_datasets['train']				,      shuffle=__SCREAMING_SNAKE_CASE				,      collate_fn=__SCREAMING_SNAKE_CASE				,      batch_size=__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Dict	      =    DataLoader(
          tokenized_datasets['validation']				,      shuffle=__SCREAMING_SNAKE_CASE				,      collate_fn=__SCREAMING_SNAKE_CASE				,      batch_size=__SCREAMING_SNAKE_CASE		)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A_			      =		mocked_dataloaders  # noqa: F811






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,      __SCREAMING_SNAKE_CASE		) == "1":
            snake_case__       :   Optional[int]	      =    2
      # New Code #
      snake_case__       :   List[str]	      =    int(args.gradient_accumulation_steps		)
      # Initialize accelerator
      snake_case__       :   Optional[Any]	      =    Accelerator(
          cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=__SCREAMING_SNAKE_CASE		)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'		)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__       :   Optional[Any]	      =    config['lr']
      snake_case__       :   Dict	      =    int(config['num_epochs']		)
      snake_case__       :   Any	      =    int(config['seed']		)
      snake_case__       :   List[str]	      =    int(config['batch_size']		)

      snake_case__       :   Tuple	      =    evaluate.load('glue'				,      'mrpc'		)

      set_seed(__SCREAMING_SNAKE_CASE		)
      snake_case__      ,							snake_case__       :   Any	      =    get_dataloaders(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__       :   Dict	      =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,      return_dict=__SCREAMING_SNAKE_CASE		)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      snake_case__       :   Dict	      =    model.to(accelerator.device		)

      # Instantiate optimizer
      snake_case__       :   int	      =    AdamW(params=model.parameters()				,      lr=__SCREAMING_SNAKE_CASE		)

      # Instantiate scheduler
      snake_case__       :   str	      =    get_linear_schedule_with_warmup(
          optimizer=__SCREAMING_SNAKE_CASE				,      num_warmup_steps=100				,      num_training_steps=(len(__SCREAMING_SNAKE_CASE		) * num_epochs)				,      )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    accelerator.prepare(
          __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      # Now we train the model
      for epoch in range(__SCREAMING_SNAKE_CASE		):
            model.train()
            for step, batch in enumerate(__SCREAMING_SNAKE_CASE		):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device		)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__SCREAMING_SNAKE_CASE		):
                        snake_case__       :   Any	      =    model(**__SCREAMING_SNAKE_CASE		)
                        snake_case__       :   Dict	      =    output.loss
                        accelerator.backward(__SCREAMING_SNAKE_CASE		)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__SCREAMING_SNAKE_CASE		):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device		)
                  with torch.no_grad():
                        snake_case__       :   Optional[int]	      =    model(**__SCREAMING_SNAKE_CASE		)
                  snake_case__       :   Optional[Any]	      =    outputs.logits.argmax(dim=-1		)
                  snake_case__      ,							snake_case__       :   int	      =    accelerator.gather_for_metrics((predictions, batch['labels'])		)
                  metric.add_batch(
                      predictions=__SCREAMING_SNAKE_CASE				,      references=__SCREAMING_SNAKE_CASE				,      )

            snake_case__       :   int	      =    metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"				,      __SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    argparse.ArgumentParser(description='Simple example of training script.'		)
      parser.add_argument(
          '--mixed_precision'				,      type=__SCREAMING_SNAKE_CASE				,      default=__SCREAMING_SNAKE_CASE				,      choices=['no', 'fp16', 'bf16', 'fp8']				,      help='Whether to use mixed precision. Choose'
          'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
          'and an Nvidia Ampere GPU.'				,      )
      # New Code #
      parser.add_argument(
          '--gradient_accumulation_steps'				,      type=__SCREAMING_SNAKE_CASE				,      default=1				,      help='The number of minibatches to be ran before gradients are accumulated.'				,      )
      parser.add_argument('--cpu'				,      action='store_true'				,      help='If passed, will train on the CPU.'		)
      snake_case__       :   Any	      =    parser.parse_args()
      snake_case__       :   Dict	      =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()
