


import math




class A						:






      def __init__(      self		:  List[str]    ,     lowercase_		:  int    ,     lowercase_		:  int    ,     lowercase_		:  int    ,     lowercase_		:  Any				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	Any						=x
             _lowerCamelCase :	Tuple						=y
             _lowerCamelCase :	Any						=step_size
             _lowerCamelCase :	List[Any]						=function_to_optimize






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.step_size
             return [
                 SearchProblem(lowercase_    ,     lowercase_    ,     lowercase_    ,     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				)      ->		Optional[int]:
             """simple docstring"""




             return hash(str(self				)				)






      def __eq__(      self		:  Tuple    ,     lowercase_		:  Optional[Any]				)      ->		int:
             """simple docstring"""




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






      def __str__(      self		:  str				)      ->		Dict:
             """simple docstring"""




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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   bool = True	,    SCREAMING_SNAKE_CASE__      :   float = math.inf	,    SCREAMING_SNAKE_CASE__      :   float = -math.inf	,    SCREAMING_SNAKE_CASE__      :   float = math.inf	,    SCREAMING_SNAKE_CASE__      :   float = -math.inf	,    SCREAMING_SNAKE_CASE__      :   bool = False	,    SCREAMING_SNAKE_CASE__      :   int = 10_000	,    ):
       '''simple docstring'''





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

       if visualization:
              from matplotlib import pyplot as plt

              plt.plot(range(SCREAMING_SNAKE_CASE__  )	,    SCREAMING_SNAKE_CASE__  )
              plt.xlabel('Iterations'  )
              plt.ylabel('Function values'  )
              plt.show()

       return current_state


if __name__ == "__main__":
 import doctest

 doctest.testmod()




 def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
        '''simple docstring'''





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

 # starting the problem with initial coordinates (3, 4)
 lowerCamelCase		      = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCamelCase		      = 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)
 lowerCamelCase		      = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 lowerCamelCase		      = 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 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   Tuple  ):
        '''simple docstring'''





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

 lowerCamelCase		      = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCamelCase		      = 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_tokenizers_available,
    is_torch_available,
)


lowerCamelCase		      = {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = ['DebertaV2TokenizerFast']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaV2ForMaskedLM',
     'TFDebertaV2ForQuestionAnswering',
     'TFDebertaV2ForSequenceClassification',
     'TFDebertaV2ForTokenClassification',
     'TFDebertaV2Model',
     'TFDebertaV2PreTrainedModel',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaV2ForMaskedLM',
     'DebertaV2ForMultipleChoice',
     'DebertaV2ForQuestionAnswering',
     'DebertaV2ForSequenceClassification',
     'DebertaV2ForTokenClassification',
     'DebertaV2Model',
     'DebertaV2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

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


