


import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def    snake_case_     (  snake_case			,							snake_case = True			,							snake_case = math.inf			,							snake_case = -math.inf			,							snake_case = math.inf			,							snake_case = -math.inf			,							snake_case = False			,							snake_case = 1_00			,							snake_case = 0.0_1			,							snake_case = 1			,							)  ->				Any:
       lowercase__:   Union[str, Any]  			=   False
       lowercase__:   Optional[Any]  			=   search_prob
       lowercase__:   Dict  			=   start_temperate
       lowercase__:   Optional[int]  			=   []
       lowercase__:   Optional[Any]  			=   0
       lowercase__:   Union[str, Any]  			=   None

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

 # starting the problem with initial coordinates (12, 47)
 __lowerCAmelCase		       =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __lowerCAmelCase		       =							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)
 __lowerCAmelCase		       =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __lowerCAmelCase		       =							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    snake_case_     (  snake_case			,							snake_case     )  ->				Union[str, Any]:
        return (3 * x**2) - (6 * y)

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

 __lowerCAmelCase		       =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __lowerCAmelCase		       =							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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a          =		{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TimesformerModel",
           "TimesformerForVideoClassification",
           "TimesformerPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timesformer import (
                  TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TimesformerForVideoClassification,
                  TimesformerModel,
                  TimesformerPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
