


import math

class 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
         snake_case					=       x
         snake_case					=       y
         snake_case					=       step_size
         snake_case					=       function_to_optimize
    def 	a_    ( self					):
         return self.function(self.x    ,      self.y					)
    def 	a_    ( self					):
         snake_case					=       self.step_size
         return [
             SearchProblem(__snake_case    ,      __snake_case    ,      __snake_case    ,      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    ,      __snake_case					):
         if isinstance(__snake_case    ,      __snake_case					):
              return hash(str(self					)					) == hash(str(__snake_case					)					)
         return False




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






def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_ = True							,UpperCamelCase_ = math.inf							,UpperCamelCase_ = -math.inf							,UpperCamelCase_ = math.inf							,UpperCamelCase_ = -math.inf							,UpperCamelCase_ = False							,UpperCamelCase_ = 1_00_00							,):



     """simple docstring"""






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



      """simple docstring"""






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

 # starting the problem with initial coordinates (3, 4)
 _SCREAMING_SNAKE_CASE    :				Dict       =					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					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)
 _SCREAMING_SNAKE_CASE    :				List[str]       =					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE    :				Tuple       =					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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



      """simple docstring"""






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

 _SCREAMING_SNAKE_CASE    :				Dict       =					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE    :				List[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 ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					AttentionProcessor

class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)




