


import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowerCAmelCase			=							logging.get_logger(__name__)




def        snake_case_					(  )  ->    Optional[int]:
       # Get the sagemaker specific mp parameters from smp_options variable.
       lowercase__:       Tuple        =       os.getenv('SM_HP_MP_PARAMETERS'      ,							'{}'					)
       try:
              # Parse it and check the field "partitions" is included, it is required for model parallel.
              lowercase__:       str        =       json.loads(snake_case					)
              if "partitions" not in smp_options:
                     return False
       except json.JSONDecodeError:
              return False

       # Get the sagemaker specific framework parameters from mpi_options variable.
       lowercase__:       Dict        =       os.getenv('SM_FRAMEWORK_PARAMS'      ,							'{}'					)
       try:
              # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
              lowercase__:       int        =       json.loads(snake_case					)
              if not mpi_options.get('sagemaker_mpi_enabled'      ,							snake_case					):
                     return False
       except json.JSONDecodeError:
              return False
       # Lastly, check if the `smdistributed` module is present.
       return importlib.util.find_spec('smdistributed'					) is not None


if is_sagemaker_model_parallel_available():
 import smdistributed.modelparallel.torch as smp

 smp.init()





@dataclass
class   __a      (   __UpperCamelCase				):
 __lowercase			:							str       					=			field(
     default=''	,	metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}	,	)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        super().__post_init__()
        warnings.warn(
            '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
            '`TrainingArguments` instead.'    ,			lowerCAmelCase__    ,			)
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    "torch.device":




        '''simple docstring'''
        logger.info('PyTorch: setting up devices'			)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
               logger.warning(
                   'torch.distributed process group is initialized, but local_rank == -1. '
                   'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'			)
        if self.no_cuda:
               lowercase__:       Union[str, Any]        =       torch.device('cpu'			)
               lowercase__:       List[str]        =       0
        elif is_sagemaker_model_parallel_available():
               lowercase__:       Optional[Any]        =       smp.local_rank()
               lowercase__:       List[Any]        =       torch.device('cuda'    ,			lowerCAmelCase__			)
               lowercase__:       str        =       1
        elif is_sagemaker_dp_enabled():
               import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

               torch.distributed.init_process_group(backend='smddp'    ,			timeout=self.ddp_timeout_delta			)
               lowercase__:       Dict        =       int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'			)			)
               lowercase__:       Dict        =       torch.device('cuda'    ,			self.local_rank			)
               lowercase__:       Optional[int]        =       1
        elif self.local_rank == -1:
               # if n_gpu is > 1 we'll use nn.DataParallel.
               # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
               # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
               # trigger an error that a device index is missing. Index 0 takes into account the
               # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
               # will use the first GPU in that env, i.e. GPU#1
               lowercase__:       Dict        =       torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
               # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
               # the default value.
               lowercase__:       List[Any]        =       torch.cuda.device_count()
        else:
               # Here, we'll use torch.distributed.
               # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
               if not torch.distributed.is_initialized():
                      torch.distributed.init_process_group(backend='nccl'    ,			timeout=self.ddp_timeout_delta			)
               lowercase__:       Optional[int]        =       torch.device('cuda'    ,			self.local_rank			)
               lowercase__:       Optional[int]        =       1

        if device.type == "cuda":
               torch.cuda.set_device(lowerCAmelCase__			)

        return device
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        if is_sagemaker_model_parallel_available():
               return smp.dp_size()

        return super().world_size
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return not is_sagemaker_model_parallel_available()
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return False






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


