






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)

__UpperCAmelCase							:   Tuple							=  logging.getLogger()





def 					lowercase_ (    __snake_case     :							List[str]      )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Dict         =   {}
				snake_case__      :Optional[Any]         =   os.path.join(lowercase__	,	"all_results.json"      )
				if os.path.exists(lowercase__      ):
								with open(lowercase__	,	"r"      ) as f:
												snake_case__      :Tuple         =   json.load(lowercase__      )
				else:
								raise ValueError(F'can\'t find {path}'      )
				return results


__UpperCAmelCase							:   str							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@require_torch_tpu
class 					_snake_case						(			__a ):






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					import xla_spawn

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

					with patch.object(lowerCAmelCase_					,"argv"					,lowerCAmelCase_       ):
									snake_case__      :List[Any]         =   time()
									xla_spawn.main()
									snake_case__      :Tuple         =   time()

									snake_case__      :Union[str, Any]         =   get_results(lowerCAmelCase_       )
									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					,500       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					import xla_spawn

					snake_case__      :str         =   "\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
					with patch.object(lowerCAmelCase_					,"argv"					,lowerCAmelCase_       ):
									xla_spawn.main()







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]