

import math





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=x
        _lowercase     	=y
        _lowercase     	=step_size
        _lowercase     	=function_to_optimize




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        return self.function(self.x							,       self.y )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=self.step_size
        return [
            SearchProblem(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       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 )  ->			Dict:
        '''simple docstring'''






        return hash(str(self ) )




  def __eq__(    self							,       lowerCAmelCase )  ->			Optional[int]:
        '''simple docstring'''






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




  def __str__(    self )  ->			Optional[int]:
        '''simple docstring'''






        return F'''x: {self.x} y: {self.y}'''
def 					a				(      A__     :       int				,       A__     :       bool = True				,       A__     :       float = math.inf				,       A__     :       float = -math.inf				,       A__     :       float = math.inf				,       A__     :       float = -math.inf				,       A__     :       bool = False				,       A__     :       int = 10000				,       )   ->				SearchProblem:





      """simple docstring"""



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

      if visualization:
            from matplotlib import pyplot as plt

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

      return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  def 					a				(      A__     :       Dict				,       A__     :       Tuple					)   ->				Optional[Any]:





        """simple docstring"""



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

  # starting the problem with initial coordinates (3, 4)
  lowercase_     =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  lowercase_     =       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)
  lowercase_     =       SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
  lowercase_     =       hill_climbing(
      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()}"
  )
  def 					a				(      A__     :       Union[str, Any]				,       A__     :       Optional[Any]					)   ->				Optional[int]:





        """simple docstring"""



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

  lowercase_     =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  lowercase_     =       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 warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


