




'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														_a      	=							inspect.getfile(accelerate.test_utils   )
														_a      	=							os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_script.py''']   )
														_a      	=							os.path.sep.join(
														    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']   )
														_a      	=							os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_ops.py''']   )







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


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
														print(F'Command: {cmd}'   )
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
														with patch_environment(omp_num_threads=1				,     cuda_visible_devices='''0,1'''   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )


if __name__ == "__main__":
				_snake_case : str  				=						Accelerator()
				_snake_case : str  				=						(accelerator.state.process_index + 2, 10)
				_snake_case : Optional[Any]  				=						torch.randint(0, 10, shape).to(accelerator.device)

				_snake_case : Dict  				=						''

				_snake_case : Optional[int]  				=						accelerator.pad_across_processes(tensor)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
								error_msg += "Padding was not done with the right value (0)."

				_snake_case : str  				=						accelerator.pad_across_processes(tensor, pad_first=True)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				_snake_case : int  				=						accelerator.state.num_processes - accelerator.state.process_index - 1
				if not torch.equal(tensora[index:], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[:index] == 0):
								error_msg += "Padding was not done with the right value (0)."

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)






'''simple docstring'''





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

A__    :   List[str]           =					logging.getLogger()





def   a_   (		_UpperCAmelCase    :      int				)    ->  Optional[int]:
      __snake_case :   List[str]													=    {}
      __snake_case :   List[str]													=    os.path.join(_UpperCAmelCase ,'all_results.json'				)
      if os.path.exists(_UpperCAmelCase				):
            with open(_UpperCAmelCase ,'r'				) as f:
                  __snake_case :   Dict													=    json.load(_UpperCAmelCase				)
      else:
            raise ValueError(f'''can\'t find {path}'''				)
      return results


A__    :   Dict           =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   int													=    self.get_auto_remove_tmp_dir()
       __snake_case :   Tuple													=    f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(__a      ,     'argv'      ,     __a  ):
             __snake_case :   Tuple													=    time()
             xla_spawn.main()
             __snake_case :   Optional[int]													=    time()

             __snake_case :   List[Any]													=    get_results(__a  )
             self.assertGreaterEqual(result['eval_accuracy']      ,     0.7_5  )

             # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
             self.assertLess(end - start      ,     500  )





 def A_   (   self						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   Optional[Any]													=    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
       with patch.object(__a      ,     'argv'      ,     __a  ):
             xla_spawn.main()



