





'''simple docstring'''






import math
import random
from typing import Any

from .hill_climbing import SearchProblem


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 = 100  ,      __SCREAMING_SNAKE_CASE = 0.0_1  ,      __SCREAMING_SNAKE_CASE = 1  ,      ):


  """simple docstring"""

  _UpperCamelCase       			=False
  _UpperCamelCase       			=search_prob
  _UpperCamelCase       			=start_temperate
  _UpperCamelCase       			=[]
  _UpperCamelCase       			=0
  _UpperCamelCase       			=None

  while not search_end:
    _UpperCamelCase       			=current_state.score()
    if best_state is None or current_score > best_state.score():
      _UpperCamelCase       			=current_state
    scores.append(__SCREAMING_SNAKE_CASE   )
    iterations += 1
    _UpperCamelCase       			=None
    _UpperCamelCase       			=current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      _UpperCamelCase       			=random.randint(0  ,      len(__SCREAMING_SNAKE_CASE   ) - 1   )  # picking a random neighbor
      _UpperCamelCase       			=neighbors.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=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       			=change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _UpperCamelCase       			=picked_neighbor
      else:
        _UpperCamelCase       			=(math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _UpperCamelCase       			=picked_neighbor
    _UpperCamelCase       			=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       			=True
    else:
      _UpperCamelCase       			=next_state

  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 best_state


if __name__ == "__main__":


      def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


        """simple docstring"""

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

      # starting the problem with initial coordinates (12, 47)
      __lowerCamelCase						: Union[str, Any]		=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase						: List[str]		=   simulated_annealing(
          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()}"""
      )

      # starting the problem with initial coordinates (12, 47)
      __lowerCamelCase						: str		=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase						: List[str]		=   simulated_annealing(
          prob, find_max=True, max_x=100, min_x=5, max_y=50, 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 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


        """simple docstring"""

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

      __lowerCamelCase						: Tuple		=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase						: Optional[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()}"""
      )

      __lowerCamelCase						: Any		=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase						: int		=   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()}"""
      )













'''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 PoolFormerImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					Union[str, Any]=400							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[str]=0.9							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					List[str]=[0.5, 0.5, 0.5]							,	)     ->						Dict:
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 30}
    _UpperCamelCase       			=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize_and_center_crop
    _UpperCamelCase       			=size
    _UpperCamelCase       			=crop_pct
    _UpperCamelCase       			=crop_size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std




  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "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   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				PoolFormerImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						int:
    _UpperCamelCase       			=PoolFormerImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize_and_center_crop'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''crop_pct'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 30}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 30, '''width''': 30}  )

    _UpperCamelCase       			=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  )     ->						Optional[Any]:
    pass


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

    # Test not batched input
    _UpperCamelCase       			=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       			=image_processing(UpperCamelCase__							,	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]  )     ->						Optional[int]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=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       			=image_processing(UpperCamelCase__							,	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    :					str  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=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       			=image_processing(UpperCamelCase__							,	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'''],
        )							,	)






