





import math





class   lowercase__     :







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







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







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		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    ):
       return hash(str(self    )    )







  def __eq__(						self     ,    _lowercase    ):
       if isinstance(_lowerCAmelCase     ,    _lowerCAmelCase    ):
            return hash(str(self    )    ) == hash(str(_lowerCAmelCase    )    )
       return False







  def __str__(						self    ):
       return F'x: {self.x} y: {self.y}'






def 				_lowerCAmelCase			(	_a :       Any       , _a :       bool = True       , _a :       float = math.inf       , _a :       float = -math.inf       , _a :       float = math.inf       , _a :       float = -math.inf       , _a :       bool = False       , _a :       int = 1_00_00       , )						->      SearchProblem:
     lowerCAmelCase_       :							Tuple			 =		search_prob
     lowerCAmelCase_       :							str			 =		[]  # list to store the current score at each iteration
     lowerCAmelCase_       :							Dict			 =		0
     lowerCAmelCase_       :							Dict			 =		False
     lowerCAmelCase_       :							int			 =		set()
     while not solution_found and iterations < max_iter:
          visited.add(UpperCAmelCase_      )
          iterations += 1
          lowerCAmelCase_       :							Optional[int]			 =		current_state.score()
          scores.append(UpperCAmelCase_      )
          lowerCAmelCase_       :							Optional[Any]			 =		current_state.get_neighbors()
          lowerCAmelCase_       :							int			 =		-math.inf
          lowerCAmelCase_       :							List[str]			 =		math.inf
          lowerCAmelCase_       :							Tuple			 =		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_       :							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:
                         lowerCAmelCase_       :							Optional[Any]			 =		change
                         lowerCAmelCase_       :							Any			 =		neighbor
               else:  # finding min
                    # to direction with greatest descent
                    if change < min_change and change < 0:
                         lowerCAmelCase_       :							List[str]			 =		change
                         lowerCAmelCase_       :							Any			 =		neighbor
          if next_state is not None:
               # we found at least one neighbor which improved the current state
               lowerCAmelCase_       :							List[Any]			 =		next_state
          else:
               # since we have no neighbor that improves the solution we stop the search
               lowerCAmelCase_       :							Tuple			 =		True

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(UpperCAmelCase_      )       , UpperCAmelCase_      )
          plt.xlabel("""Iterations"""      )
          plt.ylabel("""Function values"""      )
          plt.show()

     return current_state


if __name__ == "__main__":
      import doctest

      doctest.testmod()






      def 				_lowerCAmelCase			(	_a :       str       , _a :       Dict      )						->      Any:
           return (x**2) + (y**2)

      # starting the problem with initial coordinates (3, 4)
      UpperCAmelCase_       : Optional[Any]							=    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      UpperCAmelCase_       : str							=    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)
      UpperCAmelCase_       : str							=    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      UpperCAmelCase_       : Union[str, Any]							=    hill_climbing(
          prob, find_max=False, max_x=1_00, 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 				_lowerCAmelCase			(	_a :       Any       , _a :       List[Any]      )						->      Union[str, Any]:
           return (3 * x**2) - (6 * y)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowerCamelCase							=      {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CvtForImageClassification''',
        '''CvtModel''',
        '''CvtPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFCvtForImageClassification''',
        '''TFCvtModel''',
        '''TFCvtPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cvt import (
            CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CvtForImageClassification,
            CvtModel,
            CvtPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_cvt import (
            TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCvtForImageClassification,
            TFCvtModel,
            TFCvtPreTrainedModel,
        )


else:
    import sys

    _lowerCamelCase							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
