





from __future__ import annotations

import math



def A_      (						A__						, A__						, A__						, A__						, A__							)    ->						str:

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

       if len(_lowerCamelCase							) == 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						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase							)						, minimax(depth + 1						, node_index * 2 + 1						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase							)						, )

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



def A_      (						)    ->						Optional[int]:
       a__		:      Optional[int]					=							[90, 23, 6, 33, 21, 65, 123, 3_4423]
       a__		:      Any					=							math.log(len(_lowerCamelCase							)						, 2							)
       print('Optimal value : '						, end=''							)
       print(minimax(0						, 0						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase							)							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()









'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10				),
             MaxTimeCriteria(max_time=0.1				),
         ]				)

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

     with self.assertWarns(lowercase_				):
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			11				)

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



