
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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor





class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					str				,A   :					List[str]=7				,A   :					Optional[Any]=3				,A   :					int=18				,A   :					Union[str, Any]=30				,A   :					Dict=4_00				,A   :					int=True				,A   :					Tuple=None				,A   :					Optional[int]=True				,A   :					str=None				,A   :					Optional[Any]=True				,A   :					List[str]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,A   :					Dict=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,A   :					Tuple=True				,):
					__A       =					size if size is not None else {"height": 2_24, "width": 2_24}
					__A       =					crop_size if crop_size is not None else {"height": 18, "width": 18}
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					image_size
					__A       =					min_resolution
					__A       =					max_resolution
					__A       =					do_resize
					__A       =					size
					__A       =					do_center_crop
					__A       =					crop_size
					__A       =					do_normalize
					__A       =					image_mean
					__A       =					image_std
					__A       =					do_convert_rgb






	def 							UpperCamelCase_    (			self   :					str					):
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					}







	def 							UpperCamelCase_    (			self   :					str				,A   :					List[Any]=False				,A   :					int=False				,A   :					Optional[int]=False					):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
									__A       =					[]
									for i in range(self.batch_size					):
													image_inputs.append(
													    np.random.randint(
													        2_55				,size=(self.num_channels, self.max_resolution, self.max_resolution)				,dtype=np.uinta					)					)
					else:
									__A       =					[]
									for i in range(self.batch_size					):
													__A     ,				__A       =					np.random.choice(np.arange(self.min_resolution				,self.max_resolution					)				,2					)
													image_inputs.append(np.random.randint(2_55				,size=(self.num_channels, width, height)				,dtype=np.uinta					)					)

					if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									__A       =					[Image.fromarray(np.moveaxis(A				,0				,-1					)					) for x in image_inputs]

					if torchify:
									__A       =					[torch.from_numpy(A					) for x in image_inputs]

					return image_inputs





@require_torch
@require_vision
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	ChineseCLIPImageProcessor if is_vision_available() else None






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					ChineseCLIPImageProcessingTester(self				,do_center_crop=A					)






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"do_resize"					)					)
					self.assertTrue(hasattr(A				,"size"					)					)
					self.assertTrue(hasattr(A				,"do_center_crop"					)					)
					self.assertTrue(hasattr(A				,"center_crop"					)					)
					self.assertTrue(hasattr(A				,"do_normalize"					)					)
					self.assertTrue(hasattr(A				,"image_mean"					)					)
					self.assertTrue(hasattr(A				,"image_std"					)					)
					self.assertTrue(hasattr(A				,"do_convert_rgb"					)					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.image_processing_class.from_dict(self.image_processor_dict					)
					self.assertEqual(image_processor.size				,{"height": 2_24, "width": 2_24}					)
					self.assertEqual(image_processor.crop_size				,{"height": 18, "width": 18}					)

					__A       =					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   :					Dict					):
					pass






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					self.image_processor_tester.prepare_inputs(equal_resolution=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,Image.Image					)

					# Test not batched input
					__A       =					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
					__A       =					image_processing(A				,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   :					Any					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__A       =					self.image_processor_tester.prepare_inputs(equal_resolution=A				,numpify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,np.ndarray					)

					# Test not batched input
					__A       =					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
					__A       =					image_processing(A				,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   :					Any					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__A       =					self.image_processor_tester.prepare_inputs(equal_resolution=A				,torchify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,torch.Tensor					)

					# Test not batched input
					__A       =					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
					__A       =					image_processing(A				,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"],
					    )				,)





@require_torch
@require_vision
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	ChineseCLIPImageProcessor if is_vision_available() else None






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					ChineseCLIPImageProcessingTester(self				,num_channels=4				,do_center_crop=A					)
					__A       =					3






	@property
	def 							UpperCamelCase_    (			self   :					Dict					):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"do_resize"					)					)
					self.assertTrue(hasattr(A				,"size"					)					)
					self.assertTrue(hasattr(A				,"do_center_crop"					)					)
					self.assertTrue(hasattr(A				,"center_crop"					)					)
					self.assertTrue(hasattr(A				,"do_normalize"					)					)
					self.assertTrue(hasattr(A				,"image_mean"					)					)
					self.assertTrue(hasattr(A				,"image_std"					)					)
					self.assertTrue(hasattr(A				,"do_convert_rgb"					)					)






	def 							UpperCamelCase_    (			self   :					int					):
					pass







	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					self.image_processor_tester.prepare_inputs(equal_resolution=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,Image.Image					)

					# Test not batched input
					__A       =					image_processing(image_inputs[0]				,return_tensors="pt"					).pixel_values
					self.assertEqual(
					    encoded_images.shape				,(
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )				,)

					# Test batched
					__A       =					image_processing(A				,return_tensors="pt"					).pixel_values
					self.assertEqual(
					    encoded_images.shape				,(
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )				,)







'''simple docstring'''




from __future__ import annotations

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

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

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



