



'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     for i in range(len(__A) - 1    ,      0    ,      -1):
          _a           =    False

          for j in range(__A    ,      0    ,      -1):
               if unsorted[j] < unsorted[j - 1]:
                    _a    ,       _a           =    unsorted[j - 1], unsorted[j]
                    _a           =    True

          for j in range(__A):
               if unsorted[j] > unsorted[j + 1]:
                    _a    ,       _a           =    unsorted[j + 1], unsorted[j]
                    _a           =    True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  lowercase_									=    input("Enter numbers separated by a comma:\n").strip()
  lowercase_									=    [int(item) for item in user_input.split(",")]
  print(F"""{cocktail_shaker_sort(unsorted) = }""")








'''simple docstring'''





import unittest

import numpy as np

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

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 DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



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






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



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

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



