






from __future__ import annotations

import math

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:

  if depth < 0:
    raise ValueError('Depth cannot be less than 0'    )

  if len(UpperCAmelCase__    ) == 0:
    raise ValueError('Scores cannot be empty'    )

  if depth == height:
    return scores[node_index]

  if is_max:
    return max(
        minimax(depth + 1      ,       node_index * 2      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )      ,       minimax(depth + 1      ,       node_index * 2 + 1      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )      ,       )

  return min(
      minimax(depth + 1      ,       node_index * 2      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )      ,       minimax(depth + 1      ,       node_index * 2 + 1      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )      ,       )

def     snake_case   ()			->							None:
  UpperCamelCase_:   Dict        =							[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
  UpperCamelCase_:   Optional[int]        =							math.log(len(UpperCAmelCase__    )      ,       2    )
  print('Optimal value : '      ,       end=''    )
  print(minimax(0      ,       0      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





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__		     =     logging.getLogger()





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					__UpperCamelCase					:	Dict             =      {}
					__UpperCamelCase					:	Dict             =      os.path.join(snake_case		,	"""all_results.json"""				)
					if os.path.exists(snake_case				):
										with open(snake_case		,	"""r"""				) as f:
															__UpperCamelCase					:	List[Any]             =      json.load(snake_case				)
					else:
										raise ValueError(F'''can\'t find {path}'''				)
					return results


a__		     =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       Dict:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	Dict             =      self.get_auto_remove_tmp_dir()
						__UpperCamelCase					:	str             =      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(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Union[str, Any]             =      time()
											xla_spawn.main()
											__UpperCamelCase					:	List[str]             =      time()

											__UpperCamelCase					:	Optional[int]             =      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		:						int	)	->       List[Any]:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	List[Any]             =      """
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											xla_spawn.main()






