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)

__lowerCamelCase      :			Any							       =		logging.getLogger()



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				{}
  SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					"""all_results.json"""     )
  if os.path.exists(__UpperCamelCase     ):
    with open(__UpperCamelCase			,					"""r"""     ) as f:
      SCREAMING_SNAKE_CASE__      							=				json.load(__UpperCamelCase     )
  else:
    raise ValueError(f"""can't find {path}"""     )
  return results


__lowerCamelCase      :			Tuple							       =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     import xla_spawn

     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				time()
       xla_spawn.main()
       SCREAMING_SNAKE_CASE__      							=				time()

       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertGreaterEqual(result["""eval_accuracy"""]	,     0.75				)

       # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
       self.assertLess(end - start	,     5_00				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     import xla_spawn

     SCREAMING_SNAKE_CASE__      							=				"""
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
     with patch.object(_lowercase	,     """argv"""	,     _lowercase				):
       xla_spawn.main()

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


if __name__ == "__main__":
   __lowerCamelCase      :			Any							       =		input('''Enter numbers separated by a comma:\n''').strip()
   __lowerCamelCase      :			Dict							       =		[int(item) for item in user_input.split(''',''')]
   print(selection_sort(unsorted))
