



"""simple docstring"""
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 = 1_00 ,  __lowerCAmelCase = 0.01 ,  __lowerCAmelCase = 1 ,  ) ->						Any:







  '''simple docstring'''


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

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

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(_lowercase							) ,  _lowercase							)
    plt.xlabel("""Iterations"""							)
    plt.ylabel("""Function values"""							)
    plt.show()
  return best_state


if __name__ == "__main__":


       def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Dict:







         '''simple docstring'''


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

       # starting the problem with initial coordinates (12, 47)
       UpperCAmelCase						:		Dict			= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       UpperCAmelCase						:		Optional[Any]			= 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)
       UpperCAmelCase						:		Union[str, Any]			= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       UpperCAmelCase						:		Optional[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							) ->						List[str]:







         '''simple docstring'''


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

       UpperCAmelCase						:		Any			= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       UpperCAmelCase						:		Union[str, Any]			= 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()}"
       )

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






"""simple docstring"""
from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase						:		Union[str, Any]			= datasets.utils.logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE__ (							folder_based_builder.FolderBasedBuilderConfig   ):
    lowercase__      =   None
    lowercase__      =   None







class    SCREAMING_SNAKE_CASE__ (							folder_based_builder.FolderBasedBuilder   ):
    lowercase__      =   datasets.Audio()
    lowercase__      =   "audio"
    lowercase__      =   AudioFolderConfig
    lowercase__      =   42  # definition at the bottom of the script
    lowercase__      =   AudioClassification(audio_column="audio"      ,  label_column="label"   )







UpperCAmelCase						:		Tuple			= [
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
UpperCAmelCase						:		Any			= AUDIO_EXTENSIONS

