






import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = True					,    __lowerCAmelCase = math.inf					,    __lowerCAmelCase = -math.inf					,    __lowerCAmelCase = math.inf					,    __lowerCAmelCase = -math.inf					,    __lowerCAmelCase = False					,    __lowerCAmelCase = 100					,    __lowerCAmelCase = 0.0_1					,    __lowerCAmelCase = 1					,    ) ->	Any:
     UpperCamelCase__			:   Dict       				=  False
     UpperCamelCase__			:   str       				=  search_prob
     UpperCamelCase__			:   str       				=  start_temperate
     UpperCamelCase__			:   Tuple       				=  []
     UpperCamelCase__			:   str       				=  0
     UpperCamelCase__			:   List[str]       				=  None

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

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

 lowerCamelCase	:							Optional[int]				=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCamelCase	:							Optional[int]				=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	:							List[str]				=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCamelCase	:							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()}"""
 )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowerCamelCase	:							int				={
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ASTForAudioClassification''',
     '''ASTModel''',
     '''ASTPreTrainedModel''',
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Optional[int]				=['''ASTFeatureExtractor''']

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

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