"""simple docstring"""




import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[Any] = True			,_lowerCamelCase						:       List[Any] = math.inf			,_lowerCamelCase						:       Tuple = -math.inf			,_lowerCamelCase						:       Union[str, Any] = math.inf			,_lowerCamelCase						:       Tuple = -math.inf			,_lowerCamelCase						:       List[Any] = False			,_lowerCamelCase						:       Union[str, Any] = 100			,_lowerCamelCase						:       Optional[int] = 0.01			,_lowerCamelCase						:       str = 1			,)       ->      Union[str, Any]:
  _lowerCAmelCase						:       str  			=     False
  _lowerCAmelCase						:       Union[str, Any]  			=     search_prob
  _lowerCAmelCase						:       List[Any]  			=     start_temperate
  _lowerCAmelCase						:       Optional[int]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     0
  _lowerCAmelCase						:       Optional[int]  			=     None

  while not search_end:
    _lowerCAmelCase						:       Optional[int]  			=     current_state.score()
    if best_state is None or current_score > best_state.score():
      _lowerCAmelCase						:       List[str]  			=     current_state
    scores.append(lowerCAmelCase_	)
    iterations += 1
    _lowerCAmelCase						:       List[str]  			=     None
    _lowerCAmelCase						:       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
      _lowerCAmelCase						:       Dict  			=     random.randint(0			,len(lowerCAmelCase_	) - 1	)  # picking a random neighbor
      _lowerCAmelCase						:       Union[str, Any]  			=     neighbors.pop(lowerCAmelCase_	)
      _lowerCAmelCase						:       Union[str, 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:
        _lowerCAmelCase						:       List[Any]  			=     change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _lowerCAmelCase						:       List[str]  			=     picked_neighbor
      else:
        _lowerCAmelCase						:       List[Any]  			=     (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _lowerCAmelCase						:       Optional[Any]  			=     picked_neighbor
    _lowerCAmelCase						:       Optional[Any]  			=     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
      _lowerCAmelCase						:       Union[str, Any]  			=     True
    else:
      _lowerCAmelCase						:       List[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       int	)       ->      Any:
       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     _a      :		int           =   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     _a      :		str           =   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      :		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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Dict	)       ->      Dict:
       return (3 * x**2) - (6 * y)

     _a      :		int           =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     _a      :		Any           =   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      :		str           =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     _a      :		Optional[Any]           =   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 warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


a__				:							Any						=     (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_   )
					return {
					    "acc": acc,
					    "f1": fa,
					    "acc_and_f1": (acc + fa) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					__SCREAMING_SNAKE_CASE						   =				spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					    "corr": (pearson_corr + spearman_corr) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""
					if task_name == "cola":
										return {"mcc": matthews_corrcoef(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "sst-2":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mrpc":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "sts-b":
										return pearson_and_spearman(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "qqp":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "mnli":
										return {"mnli/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mnli-mm":
										return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "qnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "rte":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "wnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "hans":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
										raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""   )
					if task_name == "xnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )
