



"""simple docstring"""


def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					len(lowercase_		)
						for _ in range(
						    0						,      int(((no_of_elements - 1) / 2) + 1		)		):  # we don't need to traverse to end of list as
												for j in range(0						,      no_of_elements - 1		):
																		if (
																		    lst[j + 1] < lst[j]
																		):  # applying bubble sort algorithm from left to right (or forwards)
																								UpperCAmelCase			 =					lst[j + 1]
																								UpperCAmelCase			 =					lst[j]
																								UpperCAmelCase			 =					temp
																		if (
																		    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
																		):  # applying bubble sort algorithm from right to left (or backwards)
																								UpperCAmelCase			 =					lst[no_of_elements - 1 - j]
																								UpperCAmelCase			 =					lst[no_of_elements - 2 - j]
																								UpperCAmelCase			 =					temp
						return lst


if __name__ == "__main__":
				print("""enter the list to be sorted""")
				snake_case_								=			[int(x) for x in input().split()]  # inputing elements of the list in one line
				snake_case_								=			double_sort(lst)
				print("""the sorted list is""")
				print(sorted_lst)








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 ConvNextImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Optional[Any]  ,    A_			:						Union[str, Any]  ,    A_			:						List[Any]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						Optional[int]=1_8  ,    A_			:						Dict=3_0  ,    A_			:						Dict=4_0_0  ,    A_			:						List[Any]=True  ,    A_			:						int=None  ,    A_			:						List[Any]=0.875  ,    A_			:						List[str]=True  ,    A_			:						List[Any]=[0.5, 0.5, 0.5]  ,    A_			:						Optional[Any]=[0.5, 0.5, 0.5]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       Any	   =     num_channels
      _lowerCAmelCase :       str	   =     image_size
      _lowerCAmelCase :       List[Any]	   =     min_resolution
      _lowerCAmelCase :       Tuple	   =     max_resolution
      _lowerCAmelCase :       Any	   =     do_resize
      _lowerCAmelCase :       List[str]	   =     size
      _lowerCAmelCase :       List[str]	   =     crop_pct
      _lowerCAmelCase :       Tuple	   =     do_normalize
      _lowerCAmelCase :       int	   =     image_mean
      _lowerCAmelCase :       Dict	   =     image_std


 def  __magic_name__							(		self			:						Dict							):






      '''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,
          "crop_pct": self.crop_pct,
      }





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



 """simple docstring"""







 _lowercase  :       int  			=    ConvNextImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     ConvNextImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "crop_pct"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)

      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Tuple	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Any	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      _lowerCAmelCase :       str	   =     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
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Optional[Any]	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )


 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       List[str]	   =     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
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Union[str, Any]	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )






