



'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor






class 							lowercase__	(							unittest.TestCase   ):





	def __init__( self					:				Optional[Any] ,lowerCamelCase__					:				int ,lowerCamelCase__					:				Union[str, Any]=7 ,lowerCamelCase__					:				Dict=3 ,lowerCamelCase__					:				str=18 ,lowerCamelCase__					:				List[str]=30 ,lowerCamelCase__					:				str=400 ,lowerCamelCase__					:				Union[str, Any]=True ,lowerCamelCase__					:				Tuple=None ,lowerCamelCase__					:				Optional[int]=True ,lowerCamelCase__					:				int=None ,lowerCamelCase__					:				List[Any]=True ,lowerCamelCase__					:				Optional[int]=[0.5, 0.5, 0.5] ,lowerCamelCase__					:				Dict=[0.5, 0.5, 0.5] ,):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   size if size is not None else {'shortest_edge': 18}
							_UpperCamelCase					:		Union[str, Any] 				=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase					:		int 				=   parent
							_UpperCamelCase					:		Any 				=   batch_size
							_UpperCamelCase					:		List[Any] 				=   num_channels
							_UpperCamelCase					:		Optional[int] 				=   image_size
							_UpperCamelCase					:		Tuple 				=   min_resolution
							_UpperCamelCase					:		Union[str, Any] 				=   max_resolution
							_UpperCamelCase					:		List[Any] 				=   do_resize
							_UpperCamelCase					:		Optional[Any] 				=   size
							_UpperCamelCase					:		Tuple 				=   do_center_crop
							_UpperCamelCase					:		str 				=   crop_size
							_UpperCamelCase					:		str 				=   do_normalize
							_UpperCamelCase					:		str 				=   image_mean
							_UpperCamelCase					:		Optional[int] 				=   image_std





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			LevitImageProcessor if is_vision_available() else None





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   LevitImageProcessingTester(self )





	@property
	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(lowerCamelCase__ ,'image_mean' ) )
							self.assertTrue(hasattr(lowerCamelCase__ ,'image_std' ) )
							self.assertTrue(hasattr(lowerCamelCase__ ,'do_normalize' ) )
							self.assertTrue(hasattr(lowerCamelCase__ ,'do_resize' ) )
							self.assertTrue(hasattr(lowerCamelCase__ ,'do_center_crop' ) )
							self.assertTrue(hasattr(lowerCamelCase__ ,'size' ) )





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size ,{'shortest_edge': 18} )
							self.assertEqual(image_processor.crop_size ,{'height': 18, 'width': 18} )

							_UpperCamelCase					:		Any 				=   self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
							self.assertEqual(image_processor.size ,{'shortest_edge': 42} )
							self.assertEqual(image_processor.crop_size ,{'height': 84, 'width': 84} )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							pass





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase					:		List[str] 				=   self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							_UpperCamelCase					:		Optional[Any] 				=   prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ )
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,Image.Image )

							# Test not batched input
							_UpperCamelCase					:		Optional[Any] 				=   image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)

							# Test batched
							_UpperCamelCase					:		Union[str, Any] 				=   image_processing(lowerCamelCase__ ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase					:		List[str] 				=   self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							_UpperCamelCase					:		Optional[Any] 				=   prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ ,numpify=lowerCamelCase__ )
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,np.ndarray )

							# Test not batched input
							_UpperCamelCase					:		Tuple 				=   image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)

							# Test batched
							_UpperCamelCase					:		Any 				=   image_processing(lowerCamelCase__ ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase					:		int 				=   self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							_UpperCamelCase					:		Tuple 				=   prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ ,torchify=lowerCamelCase__ )
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,torch.Tensor )

							# Test not batched input
							_UpperCamelCase					:		List[Any] 				=   image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)

							# Test batched
							_UpperCamelCase					:		List[Any] 				=   image_processing(lowerCamelCase__ ,return_tensors='pt' ).pixel_values
							self.assertEqual(
							    encoded_images.shape ,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    ) ,)









from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   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_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

