




'''simple docstring'''






import math

class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   x
        lowercase	:		List[str]    =   y
        lowercase	:		int    =   step_size
        lowercase	:		Optional[int]    =   function_to_optimize



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[str]    =   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 ,		A			):






        '''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 		a_(			lowerCamelCase ,					lowerCamelCase = True ,					lowerCamelCase = math.inf ,					lowerCamelCase = -math.inf ,					lowerCamelCase = math.inf ,					lowerCamelCase = -math.inf ,					lowerCamelCase = False ,					lowerCamelCase = 1_0000 ,					)					->SearchProblem:






       '''simple docstring'''




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






             '''simple docstring'''




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

      # starting the problem with initial coordinates (3, 4)
      lowercase						:   Union[str, Any]      						=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowercase						:   Tuple      						=							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						:   Tuple      						=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowercase						:   Optional[int]      						=							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   )					->str:






             '''simple docstring'''




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

      lowercase						:   int      						=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowercase						:   Any      						=							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_sentencepiece_available, is_torch_available


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase						:   Optional[Any]      						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




