




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = True	,      UpperCamelCase = math.inf	,      UpperCamelCase = -math.inf	,      UpperCamelCase = math.inf	,      UpperCamelCase = -math.inf	,      UpperCamelCase = False	,      UpperCamelCase = 100	,      UpperCamelCase = 0.01	,      UpperCamelCase = 1	,      )     ->	Any:
  lowerCAmelCase__				:  Optional[Any]		       =     False
  lowerCAmelCase__				:  Tuple		       =     search_prob
  lowerCAmelCase__				:  Dict		       =     start_temperate
  lowerCAmelCase__				:  Optional[int]		       =     []
  lowerCAmelCase__				:  Any		       =     0
  lowerCAmelCase__				:  Any		       =     None

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_					    = {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LiltForQuestionAnswering""",
         """LiltForSequenceClassification""",
         """LiltForTokenClassification""",
         """LiltModel""",
         """LiltPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lilt import (
              LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
              LiltForQuestionAnswering,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltModel,
              LiltPreTrainedModel,
          )

else:
     import sys

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

