



'''simple docstring'''





import math






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Dict      ,     _a				:     int      ,     _a				:     int      ,     _a				:     int      ,     _a				:     List[str]       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[Any]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y
    __lowerCamelCase	:	Optional[int]   	=			step_size
    __lowerCamelCase	:	Dict   	=			function_to_optimize




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> int:
    return self.function(self.x      ,     self.y       )




  def 						_lowercase					(			self				:     Dict       ) -> List[str]:
    __lowerCamelCase	:	int   	=			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				:     Any       ) -> int:
    return hash(str(self       )       )




  def __eq__(			self				:     int      ,     _a				:     Dict       ) -> str:
    if isinstance(_a      ,     _a       ):
      return hash(str(self       )       ) == hash(str(_a       )       )
    return False




  def __str__(			self				:     Dict       ) -> Tuple:
    return f'x: {self.x} y: {self.y}'




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase = True	,_lowerCAmelCase = math.inf	,_lowerCAmelCase = -math.inf	,_lowerCAmelCase = math.inf	,_lowerCAmelCase = -math.inf	,_lowerCAmelCase = False	,_lowerCAmelCase = 10000	,)				->				SearchProblem:
  __lowerCamelCase	:	Optional[int]   	=			search_prob
  __lowerCamelCase	:	Union[str, Any]   	=			[]  # list to store the current score at each iteration
  __lowerCamelCase	:	Dict   	=			0
  __lowerCamelCase	:	List[Any]   	=			False
  __lowerCamelCase	:	Any   	=			set()
  while not solution_found and iterations < max_iter:
    visited.add(_lowerCAmelCase	)
    iterations += 1
    __lowerCamelCase	:	Optional[Any]   	=			current_state.score()
    scores.append(_lowerCAmelCase	)
    __lowerCamelCase	:	Dict   	=			current_state.get_neighbors()
    __lowerCamelCase	:	str   	=			-math.inf
    __lowerCamelCase	:	Union[str, Any]   	=			math.inf
    __lowerCamelCase	:	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
      __lowerCamelCase	:	List[Any]   	=			neighbor.score() - current_score
      if find_max:  # finding max
        # going to direction with greatest ascent
        if change > max_change and change > 0:
          __lowerCamelCase	:	str   	=			change
          __lowerCamelCase	:	Optional[Any]   	=			neighbor
      else:  # finding min
        # to direction with greatest descent
        if change < min_change and change < 0:
          __lowerCamelCase	:	Tuple   	=			change
          __lowerCamelCase	:	str   	=			neighbor
    if next_state is not None:
      # we found at least one neighbor which improved the current state
      __lowerCamelCase	:	Dict   	=			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(_lowerCAmelCase	)	,_lowerCAmelCase	)
    plt.xlabel('Iterations'	)
    plt.ylabel('Function values'	)
    plt.show()

  return current_state


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
     return (x**2) + (y**2)

   # starting the problem with initial coordinates (3, 4)
   _UpperCamelCase							    =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _UpperCamelCase							    =						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							    =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   _UpperCamelCase							    =						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_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
     return (3 * x**2) - (6 * y)

   _UpperCamelCase							    =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _UpperCamelCase							    =						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()}'''
   )











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

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






