'''simple docstring'''






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    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=[0.5, 0.5, 0.5]   ,    snake_case_=[0.5, 0.5, 0.5]   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= size if size is not None else {"""shortest_edge""": 18}
    __UpperCAmelCase:					List[Any]		= crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					str		= batch_size
    __UpperCAmelCase:					Optional[int]		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Dict		= min_resolution
    __UpperCAmelCase:					Union[str, Any]		= max_resolution
    __UpperCAmelCase:					int		= do_resize
    __UpperCAmelCase:					int		= size
    __UpperCAmelCase:					List[str]		= do_center_crop
    __UpperCAmelCase:					Optional[Any]		= crop_size
    __UpperCAmelCase:					Dict		= do_normalize
    __UpperCAmelCase:					str		= image_mean
    __UpperCAmelCase:					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    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					LevitImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= LevitImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """image_mean"""					)					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """image_std"""					)					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """do_normalize"""					)					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """do_center_crop"""					)					)
    self.assertTrue(hasattr(_lowerCAmelCase   ,    """size"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= 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}					)

    __UpperCAmelCase:					Optional[int]		= 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'''







    __UpperCAmelCase:					Union[str, Any]		= self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __UpperCAmelCase:					Any		= prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=_lowerCAmelCase					)
    for image in image_inputs:
      self.assertIsInstance(_lowerCAmelCase   ,    Image.Image					)

    # Test not batched input
    __UpperCAmelCase:					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
    __UpperCAmelCase:					Optional[Any]		= image_processing(_lowerCAmelCase   ,    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'''







    __UpperCAmelCase:					Union[str, Any]		= self.image_processing_class(**self.image_processor_dict					)
    # create random numpy tensors
    __UpperCAmelCase:					Optional[int]		= prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=_lowerCAmelCase   ,    numpify=_lowerCAmelCase					)
    for image in image_inputs:
      self.assertIsInstance(_lowerCAmelCase   ,    np.ndarray					)

    # Test not batched input
    __UpperCAmelCase:					List[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
    __UpperCAmelCase:					List[str]		= image_processing(_lowerCAmelCase   ,    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'''







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

    # Test not batched input
    __UpperCAmelCase:					List[str]		= 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
    __UpperCAmelCase:					Dict		= image_processing(_lowerCAmelCase   ,    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"""],
        )   ,    )
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list     )							->	list:
  if len(_lowercase     ) <= 1:
    return lst

  __UpperCAmelCase:					List[str]		= 1

  while i < len(_lowercase     ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        __UpperCAmelCase:					List[str]		= 1

  return lst


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('Enter numbers separated by a comma:\n').strip()
    SCREAMING_SNAKE_CASE_	   =			[int(item) for item in user_input.split(',')]
    print(gnome_sort(unsorted))