



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor



class       _snake_case   (	unittest.TestCase							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=18					,  SCREAMING_SNAKE_CASE_=30					,  SCREAMING_SNAKE_CASE_=4_00					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]					,  SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      size if size is not None else {"""shortest_edge""": 18}
   lowercase__  :   List[str]    		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   lowercase__  :   Dict    		=      parent
   lowercase__  :   Dict    		=      batch_size
   lowercase__  :   int    		=      num_channels
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Dict    		=      min_resolution
   lowercase__  :   str    		=      max_resolution
   lowercase__  :   Optional[Any]    		=      do_resize
   lowercase__  :   List[str]    		=      size
   lowercase__  :   List[str]    		=      do_center_crop
   lowercase__  :   Tuple    		=      crop_size
   lowercase__  :   Optional[Any]    		=      do_normalize
   lowercase__  :   List[Any]    		=      image_mean
   lowercase__  :   Optional[Any]    		=      image_std



 def      lowercase__     (					self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "do_center_crop": self.do_center_crop,
       "size": self.size,
       "crop_size": self.crop_size,
   }



@require_torch
@require_vision
class       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Any        =			LevitImageProcessor if is_vision_available() else None


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      LevitImageProcessingTester(self)


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """do_center_crop"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,  """size"""))


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size					,  {"""shortest_edge""": 18})
   self.assertEqual(image_processor.crop_size					,  {"""height""": 18, """width""": 18})

   lowercase__  :   str    		=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=42					,  crop_size=84)
   self.assertEqual(image_processor.size					,  {"""shortest_edge""": 42})
   self.assertEqual(image_processor.crop_size					,  {"""height""": 84, """width""": 84})


 def      lowercase__     (					self):


   '''simple docstring'''




   pass


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__  :   int    		=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_					,  Image.Image)

   # Test not batched input
   lowercase__  :   Optional[int]    		=      image_processing(image_inputs[0]					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )

   # Test batched
   lowercase__  :   str    		=      image_processing(SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__  :   List[Any]    		=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=SCREAMING_SNAKE_CASE_					,  numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_					,  np.ndarray)

   # Test not batched input
   lowercase__  :   Optional[Any]    		=      image_processing(image_inputs[0]					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )

   # Test batched
   lowercase__  :   Optional[int]    		=      image_processing(SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__  :   List[Any]    		=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=SCREAMING_SNAKE_CASE_					,  torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor)

   # Test not batched input
   lowercase__  :   Dict    		=      image_processing(image_inputs[0]					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )

   # Test batched
   lowercase__  :   List[str]    		=      image_processing(SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape					,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,  )








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 		UpperCamelCase	(      lowercase_ , lowercase_ = True , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = False , lowercase_ = 1_00 , lowercase_ = 0.01 , lowercase_ = 1 , )		->	Any:


  '''simple docstring'''






  lowercase__  :   Any    		=      False
  lowercase__  :   Union[str, Any]    		=      search_prob
  lowercase__  :   int    		=      start_temperate
  lowercase__  :   Union[str, Any]    		=      []
  lowercase__  :   List[str]    		=      0
  lowercase__  :   Optional[int]    		=      None

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


       '''simple docstring'''






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

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		str          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		List[Any]          =   simulated_annealing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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__    :		List[Any]          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Optional[int]          =   simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


       '''simple docstring'''






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

     lowerCamelCase__    :		Tuple          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		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()}'''
     )

     lowerCamelCase__    :		Dict          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Tuple          =   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()}'''
     )






