


import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase = True							,      _UpperCamelCase = math.inf							,      _UpperCamelCase = -math.inf							,      _UpperCamelCase = math.inf							,      _UpperCamelCase = -math.inf							,      _UpperCamelCase = False							,      _UpperCamelCase = 1_00							,      _UpperCamelCase = 0.01							,      _UpperCamelCase = 1							,      ):


			'''simple docstring'''







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

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


				'''simple docstring'''







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

	# starting the problem with initial coordinates (12, 47)
	a			:			Tuple												=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a			:			Optional[int]												=					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)
	a			:			Optional[int]												=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a			:			Union[str, Any]												=					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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


				'''simple docstring'''







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

	a			:			Optional[int]												=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	a			:			Optional[int]												=					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()}'''
	)

	a			:			int												=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	a			:			int												=					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'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


