




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case		=       {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ["PLBartTokenizer"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       "PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
       "PLBartForCausalLM",
       "PLBartForConditionalGeneration",
       "PLBartForSequenceClassification",
       "PLBartModel",
       "PLBartPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					bool = True,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					float = 1_00,     _lowerCAmelCase     :					float = 0.0_1,     _lowerCAmelCase     :					float = 1,     ):






     """simple docstring"""

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

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






        """simple docstring"""

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

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]       ):






        """simple docstring"""

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

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