



'''simple docstring'''





import math



class        __snake_case		:




							def __init__( self  : Dict       ,       A_  : Dict       ,       A_  : List[str]       ,       A_  : Optional[int]       ,       A_  : List[Any]):
									lowerCAmelCase_    :  Union[str, Any]             =				x
									lowerCAmelCase_    :  List[str]             =				y
									lowerCAmelCase_    :  Union[str, Any]             =				step_size
									lowerCAmelCase_    :  Tuple             =				function_to_optimize




							def      UpperCAmelCase__    ( self  : Dict):
									return self.function(self.x       ,       self.y)




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  Union[str, Any]             =				self.step_size
									return [
									    SearchProblem(__a       ,       __a       ,       __a       ,       self.function)
									    for x, y in (
									        (self.x - step_size, self.y - step_size),
									        (self.x - step_size, self.y),
									        (self.x - step_size, self.y + step_size),
									        (self.x, self.y - step_size),
									        (self.x, self.y + step_size),
									        (self.x + step_size, self.y - step_size),
									        (self.x + step_size, self.y),
									        (self.x + step_size, self.y + step_size),
									    )
									]




							def __hash__( self  : Union[str, Any]):
									return hash(str(self))




							def __eq__( self  : Union[str, Any]       ,       A_  : Tuple):





									if isinstance(__a       ,       __a):
											return hash(str(self)) == hash(str(__a))
									return False







							def __str__( self  : List[Any]):
									return F"""x: {self.x} y: {self.y}"""





def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	bool = True  ,__UpperCamelCase							:	float = math.inf  ,__UpperCamelCase							:	float = -math.inf  ,__UpperCamelCase							:	float = math.inf  ,__UpperCamelCase							:	float = -math.inf  ,__UpperCamelCase							:	bool = False  ,__UpperCamelCase							:	int = 10000  ,):
		lowerCAmelCase_    :  Optional[Any]             =				search_prob
		lowerCAmelCase_    :  List[Any]             =				[]  # list to store the current score at each iteration
		lowerCAmelCase_    :  Any             =				0
		lowerCAmelCase_    :  int             =				False
		lowerCAmelCase_    :  Tuple             =				set()
		while not solution_found and iterations < max_iter:
				visited.add(_SCREAMING_SNAKE_CASE				)
				iterations += 1
				lowerCAmelCase_    :  Optional[Any]             =				current_state.score()
				scores.append(_SCREAMING_SNAKE_CASE				)
				lowerCAmelCase_    :  List[Any]             =				current_state.get_neighbors()
				lowerCAmelCase_    :  Optional[Any]             =				-math.inf
				lowerCAmelCase_    :  str             =				math.inf
				lowerCAmelCase_    :  str             =				None  # to hold the next best neighbor
				for neighbor in neighbors:
						if neighbor in visited:
								continue  # do not want to visit the same state again
						if (
						    neighbor.x > max_x
						    or neighbor.x < min_x
						    or neighbor.y > max_y
						    or neighbor.y < min_y
						):
								continue  # neighbor outside our bounds
						lowerCAmelCase_    :  List[Any]             =				neighbor.score() - current_score
						if find_max:  # finding max
								# going to direction with greatest ascent
								if change > max_change and change > 0:
										lowerCAmelCase_    :  Dict             =				change
										lowerCAmelCase_    :  str             =				neighbor
						else:  # finding min
								# to direction with greatest descent
								if change < min_change and change < 0:
										lowerCAmelCase_    :  List[str]             =				change
										lowerCAmelCase_    :  Tuple             =				neighbor
				if next_state is not None:
						# we found at least one neighbor which improved the current state
						lowerCAmelCase_    :  Tuple             =				next_state
				else:
						# since we have no neighbor that improves the solution we stop the search
						lowerCAmelCase_    :  int             =				True

		if visualization:
				from matplotlib import pyplot as plt

				plt.plot(range(_SCREAMING_SNAKE_CASE				)  ,_SCREAMING_SNAKE_CASE				)
				plt.xlabel('''Iterations'''				)
				plt.ylabel('''Function values'''				)
				plt.show()

		return current_state


if __name__ == "__main__":
						import doctest

						doctest.testmod()


						def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Union[str, Any]				):
								return (x**2) + (y**2)

						# starting the problem with initial coordinates (3, 4)
						A__						:						Dict          =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						A__						:						Tuple          =      hill_climbing(prob, find_max=False)
						print(
						    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
						    F'''{local_min.score()}'''
						)

						# starting the problem with initial coordinates (12, 47)
						A__						:						Optional[Any]          =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						A__						:						Tuple          =      hill_climbing(
						    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
						)
						print(
						    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
						    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
						)


						def     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	Optional[Any]				):
								return (3 * x**2) - (6 * y)

						A__						:						Optional[Any]          =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						A__						:						List[Any]          =      hill_climbing(prob, find_max=True)





						print(
						    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
						    F'''{local_min.score()}'''
						)










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 DeiTImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : Optional[Any]=7       ,       A_  : str=3       ,       A_  : Optional[Any]=1_8       ,       A_  : Union[str, Any]=3_0       ,       A_  : str=4_0_0       ,       A_  : List[str]=True       ,       A_  : str=None       ,       A_  : str=True       ,       A_  : List[Any]=None       ,       A_  : List[Any]=True       ,       A_  : Optional[Any]=[0.5, 0.5, 0.5]       ,       A_  : Optional[int]=[0.5, 0.5, 0.5]       ,       ):
									lowerCAmelCase_    :  List[str]             =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									lowerCAmelCase_    :  List[Any]             =				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_    :  str             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				num_channels
									lowerCAmelCase_    :  List[str]             =				image_size
									lowerCAmelCase_    :  List[str]             =				min_resolution
									lowerCAmelCase_    :  Union[str, Any]             =				max_resolution
									lowerCAmelCase_    :  Optional[int]             =				do_resize
									lowerCAmelCase_    :  Dict             =				size
									lowerCAmelCase_    :  Dict             =				do_center_crop
									lowerCAmelCase_    :  str             =				crop_size
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				image_mean
									lowerCAmelCase_    :  Dict             =				image_std







							def      UpperCAmelCase__    ( self  : int):
									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,
									}





@require_torch
@require_vision
class        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				DeiTImageProcessor if is_vision_available() else None
							_a       =				True




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  int             =				DeiTImageProcessingTester(self)




							@property
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  List[Any]             =				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'''))




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,       {'''height''': 2_0, '''width''': 2_0})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_    :  Any             =				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=4_2       ,       crop_size=8_4)
									self.assertEqual(image_processor.size       ,       {'''height''': 4_2, '''width''': 4_2})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 8_4, '''width''': 8_4})




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									pass




							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  List[str]             =				self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_    :  Any             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       Image.Image)

									# Test not batched input
									lowerCAmelCase_    :  Union[str, 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
									lowerCAmelCase_    :  Dict             =				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  : List[str]):
									# Initialize image_processing
									lowerCAmelCase_    :  Union[str, Any]             =				self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       numpify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       np.ndarray)

									# Test not batched input
									lowerCAmelCase_    :  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
									lowerCAmelCase_    :  str             =				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
									lowerCAmelCase_    :  str             =				self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       torchify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       torch.Tensor)

									# Test not batched input
									lowerCAmelCase_    :  Dict             =				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
									lowerCAmelCase_    :  Tuple             =				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'''],
									    )       ,       )





