



"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem






def 			snake_case_    ( A_   :						Dict,							A_   :						bool = True,							A_   :						float = math.inf,							A_   :						float = -math.inf,							A_   :						float = math.inf,							A_   :						float = -math.inf,							A_   :						bool = False,							A_   :						float = 1_00,							A_   :						float = 0.01,							A_   :						float = 1,							):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					False
       _lowerCamelCase		:							str            =					search_prob
       _lowerCamelCase		:							str            =					start_temperate
       _lowerCamelCase		:							Optional[Any]            =					[]
       _lowerCamelCase		:							int            =					0
       _lowerCamelCase		:							Any            =					None

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

       if visualization:
              from matplotlib import pyplot as plt

              plt.plot(range(A_		),							A_		)
              plt.xlabel('''Iterations'''		)
              plt.ylabel('''Function values'''		)
              plt.show()
       return best_state


if __name__ == "__main__":






   def 			snake_case_    ( A_   :						int,							A_   :						Tuple		):


          '''simple docstring'''





          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=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)
   lowerCAmelCase__              =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   lowerCAmelCase__              =							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 			snake_case_    ( A_   :						Optional[int],							A_   :						List[Any]		):


          '''simple docstring'''





          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_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
