


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 GLPNImageProcessor


class     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					str		,						_lowercase      :					Any		,						_lowercase      :					Any=7		,						_lowercase      :					Union[str, Any]=3		,						_lowercase      :					Optional[int]=18		,						_lowercase      :					Optional[int]=30		,						_lowercase      :					List[str]=4_00		,						_lowercase      :					Tuple=True		,						_lowercase      :					Optional[int]=32		,						_lowercase      :					str=True		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     min_resolution
         UpperCAmelCase__										=     max_resolution
         UpperCAmelCase__										=     do_resize
         UpperCAmelCase__										=     size_divisor
         UpperCAmelCase__										=     do_rescale






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




         """simple docstring"""







         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }




@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  GLPNImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     GLPNImageProcessingTester(self )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(A__		,						"do_resize" ) )
         self.assertTrue(hasattr(A__		,						"size_divisor" ) )
         self.assertTrue(hasattr(A__		,						"resample" ) )
         self.assertTrue(hasattr(A__		,						"do_rescale" ) )






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




         """simple docstring"""







         pass






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




         """simple docstring"""







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

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCAmelCase__										=     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 (GLPNImageProcessor doesn't support batching)
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCAmelCase__										=     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 (GLPNImageProcessor doesn't support batching)
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


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





