




'''simple docstring'''


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


_snake_case : Any  				=						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       (						_a       ):







											def __init__(    self			:    Any				,     lowerCAmelCase_			:    int = 1_01   )     ->				Dict:


																		"""simple docstring"""


																		_a      	=							length







											def __len__(    self			:    Optional[Any]   )     ->				Any:


																		"""simple docstring"""


																		return self.length







											def __getitem__(    self			:    Tuple				,     lowerCAmelCase_			:    List[Any]   )     ->				int:


																		"""simple docstring"""


																		return i



				class      A       :







											def __call__(    self			:    List[str]				,     lowerCAmelCase_			:    Any   )     ->				str:


																		"""simple docstring"""


																		return {"input_ids": torch.tensor(lowerCAmelCase_   ), "labels": torch.tensor(lowerCAmelCase_   )}



				class      A       (						nn.Module       ):







											def __init__(    self			:    Any   )     ->				Tuple:


																		"""simple docstring"""


																		super().__init__()
																		# Add some (unused) params otherwise DDP will complain.
																		_a      	=							nn.Linear(1_20				,     80   )







											def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    List[str]=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       (						_a       ):







							@require_torch_neuroncore
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
														_a      	=							self.get_auto_remove_tmp_dir()
														_a      	=							F'--output_dir {output_dir}'.split()
														_a      	=							['''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       (						_a       ):







							@require_torch_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							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()
														_a      	=							self.get_auto_remove_tmp_dir()
														_a      	=							F'--output_dir {output_dir}'.split()
														_a      	=							['''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

				_snake_case : List[str]  				=						HfArgumentParser((TrainingArguments,))
				_snake_case : Optional[Any]  				=						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 [101, 40, 7]:
								_snake_case : Any  				=						DummyDataset(dataset_length)
								def 						snake_case_       (UpperCamelCase			:    EvalPrediction ):





															'''simple docstring'''


															_a      	=							list(range(len(UpperCamelCase ) ) )
															_a      	=							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}

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

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

								_snake_case : Any  				=						2

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

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

								_snake_case : Optional[Any]  				=						None









'''simple docstring'''



from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__UpperCamelCase   :							Optional[int]         =   Lock()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      int:

   """simple docstring"""
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0,       10     ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         __a					  =     rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         __a					  =     min(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         __a					  =     lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         __a					  =     max(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     []
   __a					  =     []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()     )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   __a					  =     Pipe()
   __a					  =     Pipe()
   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),       )     )
   __a					  =     temp_rs
   __a					  =     temp_rr

   for i in range(1,       len(SCREAMING_SNAKE_CASE__     ) - 1     ):
      __a					  =     Pipe()
      __a					  =     Pipe()
      process_array_.append(
          Process(
              target=SCREAMING_SNAKE_CASE__,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),       )     )
      __a					  =     temp_rs
      __a					  =     temp_rr

   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(
               len(SCREAMING_SNAKE_CASE__     ) - 1,
               arr[len(SCREAMING_SNAKE_CASE__     ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(SCREAMING_SNAKE_CASE__     ) - 1],
           ),       )     )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0,       len(SCREAMING_SNAKE_CASE__     )     ):
      __a					  =     result_pipe[p][0].recv()
      process_array_[p].join()
   return arr



def  __UpperCAmelCase						(					)	->      Any:

   """simple docstring"""
   __a					  =     list(range(10,       0,       -1     )     )
   print('Initial List'     )
   print(*SCREAMING_SNAKE_CASE__     )
   __a					  =     odd_even_transposition(SCREAMING_SNAKE_CASE__     )
   print('Sorted List\n'     )
   print(*SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()