


'''simple docstring'''

import math
import random
from typing import Any

from .hill_climbing import SearchProblem






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: bool = True      ,  __lowerCamelCase		: float = math.inf      ,  __lowerCamelCase		: float = -math.inf      ,  __lowerCamelCase		: float = math.inf      ,  __lowerCamelCase		: float = -math.inf      ,  __lowerCamelCase		: bool = False      ,  __lowerCamelCase		: float = 100      ,  __lowerCamelCase		: float = 0.01      ,  __lowerCamelCase		: float = 1      ,  )      ->     Any:







     '''simple docstring'''






     _a						=		False
     _a						=		search_prob
     _a						=		start_temperate
     _a						=		[]
     _a						=		0
     _a						=		None

     while not search_end:
          _a						=		current_state.score()
          if best_state is None or current_score > best_state.score():
               _a						=		current_state
          scores.append(__lowerCamelCase				)
          iterations += 1
          _a						=		None
          _a						=		current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               _a						=		random.randint(0      ,  len(__lowerCamelCase				) - 1				)  # picking a random neighbor
               _a						=		neighbors.pop(__lowerCamelCase				)
               _a						=		picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    _a						=		change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    _a						=		picked_neighbor
               else:
                    _a						=		(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         _a						=		picked_neighbor
          _a						=		current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               _a						=		True
          else:
               _a						=		next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(__lowerCamelCase				)      ,  __lowerCamelCase				)
          plt.xlabel("Iterations"				)
          plt.ylabel("Function values"				)
          plt.show()
     return best_state


if __name__ == "__main__":






   def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Union[str, Any]				)      ->     Union[str, Any]:







        '''simple docstring'''






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

   # starting the problem with initial coordinates (12, 47)
   lowercase__				     =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   lowercase__				     =		simulated_annealing(
       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()}'''
   )

   # starting the problem with initial coordinates (12, 47)
   lowercase__				     =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   lowercase__				     =		simulated_annealing(
       prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
   )
   print(
       "The maximum 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   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]				)      ->     Optional[Any]:







        '''simple docstring'''






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

   lowercase__				     =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   lowercase__				     =		simulated_annealing(prob, find_max=False, visualization=True)
   print(
       "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
       f'''{local_min.score()}'''
   )

   lowercase__				     =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   lowercase__				     =		simulated_annealing(prob, find_max=True, visualization=True)






   print(
       "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
       f'''{local_min.score()}'''
   )





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ,			lowerCamelCase__ ):






     def  a_					(			self		)       ->    Tuple:
          _a						=		load_tool("text-classification"		)
          self.tool.setup()
          _a						=		load_tool("text-classification"     ,							remote=__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.remote_tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    str:
          _a						=		self.remote_tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)

