



'''simple docstring'''
import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[int]					,   _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 = 1_0_0					,   _SCREAMING_SNAKE_CASE     :  float = 0.0_1					,   _SCREAMING_SNAKE_CASE     :  float = 1					,   ):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    List[str]						    =							False
					__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							search_prob
					__SCREAMING_SNAKE_CASE				:    Any						    =							start_temperate
					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							[]
					__SCREAMING_SNAKE_CASE				:    List[str]						    =							0
					__SCREAMING_SNAKE_CASE				:    str						    =							None

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

					if visualization:
										from matplotlib import pyplot as plt

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


if __name__ == "__main__":



					def        __A   (							_SCREAMING_SNAKE_CASE     :  Any					,   _SCREAMING_SNAKE_CASE     :  Dict	):







										"""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        __A   (							_SCREAMING_SNAKE_CASE     :  int					,   _SCREAMING_SNAKE_CASE     :  List[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 pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict''',   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''',   num_bytes=1_337,   num_examples=42,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train''',   num_bytes=1_337,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ],   )
def        a__     (					UpperCamelCase_						:      SplitDict						):
   UpperCAmelCase__	:List[Any]      =      split_dict._to_yaml_list()
   assert len(UpperCamelCase_						) == len(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      SplitDict._from_yaml_list(UpperCamelCase_						)
   for split_name, split_info in split_dict.items():
      # dataset_name field is deprecated, and is therefore not part of the YAML dump
      UpperCAmelCase__	:Union[str, Any]      =      None
      # the split name of split_dict takes over the name of the split info object
      UpperCAmelCase__	:Union[str, Any]      =      split_name
   assert split_dict == reloaded


@pytest.mark.parametrize(
    '''split_info''',   [SplitInfo(), SplitInfo(dataset_name=UpperCamelCase_						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def        a__     (					UpperCamelCase_						:      List[Any]						):
   # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
   # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
   UpperCAmelCase__	:List[Any]      =      asdict(SplitDict({'''train''': split_info}						)						)
   assert "dataset_name" in split_dict_asdict["train"]
   assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name



