


import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 						lowerCamelCase(   a__	,a__ = True	,a__ = math.inf	,a__ = -math.inf	,a__ = math.inf	,a__ = -math.inf	,a__ = False	,a__ = 100	,a__ = 0.01	,a__ = 1	,):
				_SCREAMING_SNAKE_CASE			  =False
				_SCREAMING_SNAKE_CASE			  =search_prob
				_SCREAMING_SNAKE_CASE			  =start_temperate
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =0
				_SCREAMING_SNAKE_CASE			  =None

				while not search_end:
								_SCREAMING_SNAKE_CASE			  =current_state.score()
								if best_state is None or current_score > best_state.score():
												_SCREAMING_SNAKE_CASE			  =current_state
								scores.append(a__)
								iterations += 1
								_SCREAMING_SNAKE_CASE			  =None
								_SCREAMING_SNAKE_CASE			  =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			  =random.randint(0	,len(a__) - 1)  # picking a random neighbor
												_SCREAMING_SNAKE_CASE			  =neighbors.pop(a__)
												_SCREAMING_SNAKE_CASE			  =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			  =change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																_SCREAMING_SNAKE_CASE			  =picked_neighbor
												else:
																_SCREAMING_SNAKE_CASE			  =(math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				_SCREAMING_SNAKE_CASE			  =picked_neighbor
								_SCREAMING_SNAKE_CASE			  =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			  =True
								else:
												_SCREAMING_SNAKE_CASE			  =next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(a__)	,a__)
								plt.xlabel('''Iterations''')
								plt.ylabel('''Function values''')
								plt.show()
				return best_state


if __name__ == "__main__":







							def 						lowerCamelCase(   a__	,a__):
											return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							snake_case_						:			str													=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							snake_case_						:			Any													=							simulated_annealing(
							    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()}"""
							)

							# starting the problem with initial coordinates (12, 47)
							snake_case_						:			str													=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							snake_case_						:			List[str]													=							simulated_annealing(
							    prob, find_max=True, max_x=1_00, 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 						lowerCamelCase(   a__	,a__):
											return (3 * x**2) - (6 * y)

							snake_case_						:			Tuple													=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							snake_case_						:			Dict													=							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_						:			List[str]													=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							snake_case_						:			Tuple													=							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 importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case_						:			Optional[Any]													=							'''scheduler_config.json'''

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						1
						UpperCAmelCase             =						2
						UpperCAmelCase             =						3
						UpperCAmelCase             =						4
						UpperCAmelCase             =						5
						UpperCAmelCase             =						6
						UpperCAmelCase             =						7
						UpperCAmelCase             =						8
						UpperCAmelCase             =						9
						UpperCAmelCase             =						10
						UpperCAmelCase             =						11
						UpperCAmelCase             =						12
						UpperCAmelCase             =						13
						UpperCAmelCase             =						14

@dataclass
class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						42

class 						A__							:
						UpperCAmelCase             =						SCHEDULER_CONFIG_NAME
						UpperCAmelCase             =						[]
						UpperCAmelCase             =						True




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[str]				,    _a					:      Dict[str, Any] = None				,    _a					:      Optional[str] = None				,    _a					:      Optional[Any]=False				,    **_a					:      Dict				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =cls.load_config(
										    pretrained_model_name_or_path=_a				,    subfolder=_a				,    return_unused_kwargs=_a				,    return_commit_hash=_a				,    **_a				,    )
										return cls.from_config(_a				,    return_unused_kwargs=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Union[str, os.PathLike]				,    _a					:      bool = False				,    **_a					:      int  )							->     Dict:


										"""simple docstring"""





										self.save_config(save_directory=_a				,    push_to_hub=_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[Any]:


										"""simple docstring"""





										return self._get_compatibles()




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =list(set([cls.__name__] + cls._compatibles  )  )
										_SCREAMING_SNAKE_CASE			  =importlib.import_module(__name__.split('''.'''  )[0]  )
										_SCREAMING_SNAKE_CASE			  =[
										    getattr(_a				,    _a  ) for c in compatible_classes_str if hasattr(_a				,    _a  )
										]
										return compatible_classes