
import math


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self  , A  , A  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:int 		=    x
									UpperCAmelCase__		:List[str] 		=    y
									UpperCAmelCase__		:List[Any] 		=    step_size
									UpperCAmelCase__		:Optional[Any] 		=    function_to_optimize


						def    A__       (  self	)  ->int:
									return self.function(self.x  , self.y	)


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:str 		=    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	)  ->List[str]:
									return hash(str(self	)	)


						def __eq__(  self  , A	)  ->Optional[int]:
									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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE = True     ,						SCREAMING_SNAKE_CASE = math.inf     ,						SCREAMING_SNAKE_CASE = -math.inf     ,						SCREAMING_SNAKE_CASE = math.inf     ,						SCREAMING_SNAKE_CASE = -math.inf     ,						SCREAMING_SNAKE_CASE = False     ,						SCREAMING_SNAKE_CASE = 10000     ,						):




			"""simple docstring"""


			UpperCAmelCase__		:List[Any] 		=    search_prob
			UpperCAmelCase__		:Tuple 		=    []  # list to store the current score at each iteration
			UpperCAmelCase__		:List[str] 		=    0
			UpperCAmelCase__		:Tuple 		=    False
			UpperCAmelCase__		:int 		=    set()
			while not solution_found and iterations < max_iter:
						visited.add(SCREAMING_SNAKE_CASE							)
						iterations += 1
						UpperCAmelCase__		:List[str] 		=    current_state.score()
						scores.append(SCREAMING_SNAKE_CASE							)
						UpperCAmelCase__		:Union[str, Any] 		=    current_state.get_neighbors()
						UpperCAmelCase__		:int 		=    -math.inf
						UpperCAmelCase__		:str 		=    math.inf
						UpperCAmelCase__		:Any 		=    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
									UpperCAmelCase__		:Union[str, Any] 		=    neighbor.score() - current_score
									if find_max:  # finding max
												# going to direction with greatest ascent
												if change > max_change and change > 0:
															UpperCAmelCase__		:Tuple 		=    change
															UpperCAmelCase__		:Union[str, Any] 		=    neighbor
									else:  # finding min
												# to direction with greatest descent
												if change < min_change and change < 0:
															UpperCAmelCase__		:Union[str, Any] 		=    change
															UpperCAmelCase__		:Optional[int] 		=    neighbor
						if next_state is not None:
									# we found at least one neighbor which improved the current state
									UpperCAmelCase__		:List[Any] 		=    next_state
						else:
									# since we have no neighbor that improves the solution we stop the search
									UpperCAmelCase__		:Optional[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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




									"""simple docstring"""


									return (x**2) + (y**2)

						# starting the problem with initial coordinates (3, 4)
						__snake_case					:							str           =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__snake_case					:							Any           =							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)
						__snake_case					:							List[Any]           =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						__snake_case					:							str           =							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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




									"""simple docstring"""


									return (3 * x**2) - (6 * y)

						__snake_case					:							Union[str, Any]           =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__snake_case					:							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 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

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


						def    A__       (  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,
									}


@require_torch
@require_vision
class 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    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    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}	)
									self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}	)

									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict  , size=42  , crop_size=84	)
									self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}	)
									self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}	)


						def    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , Image.Image	)

									# Test not batched input
									UpperCAmelCase__		:int 		=    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[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    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    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
									UpperCAmelCase__		:List[str] 		=    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__		:Optional[Any] 		=    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    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    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
									UpperCAmelCase__		:List[str] 		=    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__		:Optional[int] 		=    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'],
									    )  , )





