




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      :  List[str]      						=						logging.getLogger()




def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]       ):
				_lowerCAmelCase    :    Union[str, Any]										=      {}
				_lowerCAmelCase    :    int										=      os.path.join(UpperCamelCase_      ,    """all_results.json"""       )
				if os.path.exists(UpperCamelCase_       ):
								with open(UpperCamelCase_      ,    """r"""       ) as f:
												_lowerCAmelCase    :    int										=      json.load(UpperCamelCase_       )
				else:
								raise ValueError(F"can\'t find {path}"       )
				return results


_lowerCamelCase      :  int      						=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class        __snake_case  (_SCREAMING_SNAKE_CASE		):



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[Any]:





								'''simple docstring'''




								import xla_spawn

								_lowerCAmelCase    :    int										=      self.get_auto_remove_tmp_dir()
								_lowerCAmelCase    :    List[str]										=      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(A_		,	"""argv"""		,	A_      ):
												_lowerCAmelCase    :    List[Any]										=      time()
												xla_spawn.main()
												_lowerCAmelCase    :    str										=      time()

												_lowerCAmelCase    :    Tuple										=      get_results(A_      )
												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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								import xla_spawn

								_lowerCAmelCase    :    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()







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      [int(UpperCamelCase_       ) for i in ip_va_address.split("""."""       ) if i.isdigit()]
				return len(UpperCamelCase_       ) == 4 and all(0 <= int(UpperCamelCase_       ) <= 254 for octet in octets       )


if __name__ == "__main__":
	_lowerCamelCase      :  List[str]      						=						input().strip()
	_lowerCamelCase      :  int      						=						"valid" if is_ip_va_address_valid(ip) else "invalid"
	print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')

