

import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    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_		:    float = 100	,     SCREAMING_SNAKE_CASE_		:    float = 0.01	,     SCREAMING_SNAKE_CASE_		:    float = 1	,     ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       False
     lowercase__    :   Tuple      =       search_prob
     lowercase__    :   str      =       start_temperate
     lowercase__    :   str      =       []
     lowercase__    :   Optional[int]      =       0
     lowercase__    :   Union[str, Any]      =       None

     while not search_end:
          lowercase__    :   Optional[Any]      =       current_state.score()
          if best_state is None or current_score > best_state.score():
               lowercase__    :   Optional[int]      =       current_state
          scores.append(SCREAMING_SNAKE_CASE_			)
          iterations += 1
          lowercase__    :   List[Any]      =       None
          lowercase__    :   Union[str, Any]      =       current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               lowercase__    :   Any      =       random.randint(0	,     len(SCREAMING_SNAKE_CASE_			) - 1			)  # picking a random neighbor
               lowercase__    :   Any      =       neighbors.pop(SCREAMING_SNAKE_CASE_			)
               lowercase__    :   Tuple      =       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:
                    lowercase__    :   int      =       change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    lowercase__    :   Tuple      =       picked_neighbor
               else:
                    lowercase__    :   int      =       (math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         lowercase__    :   str      =       picked_neighbor
          lowercase__    :   str      =       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
               lowercase__    :   Union[str, Any]      =       True
          else:
               lowercase__    :   List[Any]      =       next_state

     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 best_state


if __name__ == "__main__":

       def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict	,     SCREAMING_SNAKE_CASE_		:    List[Any]			):



            '''simple docstring'''





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

       # starting the problem with initial coordinates (12, 47)
       snake_case_			  = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       snake_case_			  = 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)
       snake_case_			  = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       snake_case_			  = 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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    List[Any]			):



            '''simple docstring'''





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

       snake_case_			  = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       snake_case_			  = 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()}'''
       )

       snake_case_			  = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       snake_case_			  = 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()}'''
       )




import unittest

from transformers import DonutProcessor


snake_case_			  = '''naver-clova-ix/donut-base'''


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       DonutProcessor.from_pretrained(a)





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       {
             'name': 'John Doe',
             'age': '99',
             'city': 'Atlanta',
             'state': 'GA',
             'zip': '30301',
             'phone': '123-4567',
             'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
         }

         lowercase__    :   Tuple      =       (
             '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
             '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
             '<s_nicknames><s_nickname>Johnny</s_nickname>'
             '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
         )
         lowercase__    :   str      =       self.processor.tokenajson(a)

         self.assertDictEqual(a						,   a)

