



'''simple docstring'''




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class     UpperCamelCase_				(							unittest.TestCase    ):





	def __init__(						self	,	A	,	A = True	,	A = None	,	A = 32	,	A = True	,	A = 1 / 255	,	A = True	,	A = True	,	A = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,	A = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,	A = True	,	A=7	,	A=30	,	A=400	,	A=3	,	)    ->    List[Any]:
								UpperCAmelCase  :	Optional[Any]							  =	parent
								UpperCAmelCase  :	List[Any]							  =	do_resize
								UpperCAmelCase  :	Dict							  =	size if size is not None else {'shortest_edge': 288}
								UpperCAmelCase  :	str							  =	size_divisor
								UpperCAmelCase  :	List[str]							  =	do_rescale
								UpperCAmelCase  :	int							  =	rescale_factor
								UpperCAmelCase  :	Dict							  =	do_normalize
								UpperCAmelCase  :	str							  =	do_center_crop
								UpperCAmelCase  :	Optional[int]							  =	image_mean
								UpperCAmelCase  :	Dict							  =	image_std
								UpperCAmelCase  :	int							  =	do_pad
								UpperCAmelCase  :	Union[str, Any]							  =	batch_size
								UpperCAmelCase  :	Tuple							  =	num_channels
								UpperCAmelCase  :	List[str]							  =	min_resolution
								UpperCAmelCase  :	Dict							  =	max_resolution





	def 				_lowercase(						self					)    ->    List[Any]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}






	def 				_lowercase(						self	,	A	,	A=False					)    ->    Optional[int]:

								if not batched:
															UpperCAmelCase  :	Optional[Any]							  =	self.size['shortest_edge']
															UpperCAmelCase  :	Tuple							  =	image_inputs[0]
															if isinstance(_A	,	Image.Image					):
																						UpperCAmelCase  :	Optional[Any]							  =	image.size
															else:
																						UpperCAmelCase  :	Union[str, Any]							  =	image.shape[1], image.shape[2]
															UpperCAmelCase  :	Union[str, Any]							  =	size / min(_A	,	_A					)
															if h < w:
																						UpperCAmelCase  :	List[str]							  =	size, scale * w
															else:
																						UpperCAmelCase  :	Tuple							  =	scale * h, size

															UpperCAmelCase  :	Optional[int]							  =	int((1333 / 800) * size					)
															if max(_A	,	_A					) > max_size:
																						UpperCAmelCase  :	Union[str, Any]							  =	max_size / max(_A	,	_A					)
																						UpperCAmelCase  :	Optional[Any]							  =	newh * scale
																						UpperCAmelCase  :	Any							  =	neww * scale

															UpperCAmelCase  :	Union[str, Any]							  =	int(newh + 0.5					), int(neww + 0.5					)
															UpperCAmelCase  :	Optional[Any]							  =	(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

								else:
															UpperCAmelCase  :	List[Any]							  =	[]
															for image in image_inputs:
																						UpperCAmelCase  :	int							  =	self.get_expected_values([image]					)
																						expected_values.append((expected_height, expected_width)					)
															UpperCAmelCase  :	Union[str, Any]							  =	max(_A	,	key=lambda A					: item[0]					)[0]
															UpperCAmelCase  :	List[str]							  =	max(_A	,	key=lambda A					: item[1]					)[1]

								return expected_height, expected_width




@require_torch
@require_vision
class     UpperCamelCase_				(							a__   ,  unittest.TestCase    ):
	lowercase    =				BridgeTowerImageProcessor if is_vision_available() else None





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Optional[int]							  =	BridgeTowerImageProcessingTester(self					)





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





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(_A	,	"""image_mean"""					)					)
								self.assertTrue(hasattr(_A	,	"""image_std"""					)					)
								self.assertTrue(hasattr(_A	,	"""do_normalize"""					)					)
								self.assertTrue(hasattr(_A	,	"""do_resize"""					)					)
								self.assertTrue(hasattr(_A	,	"""size"""					)					)
								self.assertTrue(hasattr(_A	,	"""size_divisor"""					)					)





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								pass





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Optional[int]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								UpperCAmelCase  :	List[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
								UpperCAmelCase  :	Optional[Any]							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	List[Any]							  =	self.image_processor_tester.get_expected_values(_A					)
								self.assertEqual(
								    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

								# Test batched
								UpperCAmelCase  :	Optional[int]							  =	image_processing(_A	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processor_tester.get_expected_values(_A	,	batched=_A					)
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,	)





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	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  :	int							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	Optional[Any]							  =	self.image_processor_tester.get_expected_values(_A					)
								self.assertEqual(
								    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

								# Test batched
								UpperCAmelCase  :	str							  =	image_processing(_A	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	List[Any]							  =	self.image_processor_tester.get_expected_values(_A	,	batched=_A					)
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,	)






	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	List[Any]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								UpperCAmelCase  :	Optional[int]							  =	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  :	int							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	int							  =	self.image_processor_tester.get_expected_values(_A					)
								self.assertEqual(
								    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

								# Test batched
								UpperCAmelCase  :	List[str]							  =	image_processing(_A	,	return_tensors="""pt"""					).pixel_values

								UpperCAmelCase  :	Optional[int]							  =	self.image_processor_tester.get_expected_values(_A	,	batched=_A					)
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,	)






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

							while not search_end:
														UpperCAmelCase  :	List[str]							  =	current_state.score()
														if best_state is None or current_score > best_state.score():
																					UpperCAmelCase  :	List[Any]							  =	current_state
														scores.append(_lowercase	)
														iterations += 1
														UpperCAmelCase  :	Dict							  =	None
														UpperCAmelCase  :	Union[str, Any]							  =	current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					UpperCAmelCase  :	int							  =	random.randint(0			,       len(_lowercase	) - 1	)  # picking a random neighbor
																					UpperCAmelCase  :	int							  =	neighbors.pop(_lowercase	)
																					UpperCAmelCase  :	Tuple							  =	picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												UpperCAmelCase  :	Union[str, Any]							  =	change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												UpperCAmelCase  :	int							  =	picked_neighbor
																					else:
																												UpperCAmelCase  :	Optional[Any]							  =	(math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			UpperCAmelCase  :	Optional[int]							  =	picked_neighbor
														UpperCAmelCase  :	List[Any]							  =	current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					UpperCAmelCase  :	Optional[int]							  =	True
														else:
																					UpperCAmelCase  :	Optional[int]							  =	next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(_lowercase	)			,       _lowercase	)
														plt.xlabel("""Iterations"""	)
														plt.ylabel("""Function values"""	)
														plt.show()
							return best_state


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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()}'''
							)

							# starting the problem with initial coordinates (12, 47)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The maximum 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    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

							a     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(prob, find_max=True, visualization=True)


							print(
							    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

