



import math





class       __lowerCAmelCase				:




    def __init__(							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



         """simple docstring"""





         _lowerCAmelCase      			=					x
         _lowerCAmelCase      			=					y
         _lowerCAmelCase      			=					step_size
         _lowerCAmelCase      			=					function_to_optimize




    def 							snake_case     (							self	):



         """simple docstring"""





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




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					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	):



         """simple docstring"""





         return hash(str(self	)	)




    def __eq__(							self		,       _snake_case	):



         """simple docstring"""





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




    def __str__(							self	):



         """simple docstring"""





         return F'x: {self.x} y: {self.y}'


def 	_UpperCAmelCase       (   snake_case			,		snake_case = True			,		snake_case = math.inf			,		snake_case = -math.inf			,		snake_case = math.inf			,		snake_case = -math.inf			,		snake_case = False			,		snake_case = 1_00_00			,		):





     """simple docstring"""

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





      """simple docstring"""

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

 # starting the problem with initial coordinates (3, 4)
 A__			   = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 A__			   = 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)
 A__			   = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 A__			   = 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       (   snake_case			,		snake_case    ):





      """simple docstring"""

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

 A__			   = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 A__			   = 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_torch_available, is_vision_available


A__			   = {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = ["""VideoMAEFeatureExtractor"""]
	A__			   = ["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



