'''simple docstring'''

import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase = True		,					UpperCAmelCase = math.inf		,					UpperCAmelCase = -math.inf		,					UpperCAmelCase = math.inf		,					UpperCAmelCase = -math.inf		,					UpperCAmelCase = False		,					UpperCAmelCase = 100		,					UpperCAmelCase = 0.0_1		,					UpperCAmelCase = 1		,					):
 lowercase__    :   Union[str, Any]						= False
 lowercase__    :   int						= search_prob
 lowercase__    :   Optional[Any]						= start_temperate
 lowercase__    :   List[Any]						= []
 lowercase__    :   Tuple						= 0
 lowercase__    :   Optional[Any]						= None

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

  # starting the problem with initial coordinates (12, 47)
  __a:			Optional[Any]	       =       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  __a:			List[Any]	       =       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)
  __a:			List[str]	       =       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  __a:			Union[str, Any]	       =       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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
   return (3 * x**2) - (6 * y)

  __a:			Optional[int]	       =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  __a:			Dict	       =       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()}'
  )

  __a:			Union[str, Any]	       =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  __a:			Dict	       =       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'''

import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @require_torch
       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   str						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''hf-internal-testing/tiny-clap-htsat-unfused'''			)
        lowercase__    :   Optional[Any]						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   Tuple						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Optional[Any]						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]     ,       )

       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  str:
        pass

       @slow
       @require_torch
       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[str]						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''laion/clap-htsat-unfused'''     ,       )
        # This is an audio of a dog
        lowercase__    :   int						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   str						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Any						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)

        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
            ]     ,       )

        lowercase__    :   Dict						= audio_classifier([audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )
        lowercase__    :   Any						= audio_classifier(
            [audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']     ,       batch_size=5			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )


       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass

