


"""simple docstring"""






import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=7  ,					__UpperCAmelCase=3  ,					__UpperCAmelCase=30  ,					__UpperCAmelCase=4_00  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=[0.5, 0.5, 0.5]  ,					__UpperCAmelCase=[0.5, 0.5, 0.5]  ,					__UpperCAmelCase=False  ,					)				->					Optional[Any]:
     a   :	Any						=    size if size is not None else {'height': 30, 'width': 30}
     a   :	Optional[Any]						=    parent
     a   :	Optional[int]						=    batch_size
     a   :	Tuple						=    num_channels
     a   :	Union[str, Any]						=    min_resolution
     a   :	Union[str, Any]						=    max_resolution
     a   :	Any						=    do_resize
     a   :	int						=    size
     a   :	Union[str, Any]						=    do_normalize
     a   :	Optional[int]						=    image_mean
     a   :	List[Any]						=    image_std
     a   :	str						=    do_reduce_labels






  def 							lowercase_     (						self						)				->					List[Any]:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_reduce_labels": self.do_reduce_labels,
     }




def        A_					(					)     ->     Optional[int]:
   a   :	Any						=    load_dataset('hf-internal-testing/fixtures_ade20k'       ,   split='test'				)

   a   :	Optional[Any]						=    Image.open(dataset[0]['file']				)
   a   :	Optional[Any]						=    Image.open(dataset[1]['file']				)

   return image, map




def        A_					(					)     ->     Any:
   a   :	Union[str, Any]						=    load_dataset('hf-internal-testing/fixtures_ade20k'       ,   split='test'				)

   a   :	List[str]						=    Image.open(dataset[0]['file']				)
   a   :	List[str]						=    Image.open(dataset[1]['file']				)
   a   :	Dict						=    Image.open(dataset[2]['file']				)
   a   :	Union[str, Any]						=    Image.open(dataset[3]['file']				)

   return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 	A_       (     _UpperCAmelCase      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	int   =						SegformerImageProcessor if is_vision_available() else None
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	List[str]						=    SegformerImageProcessingTester(self						)
  @property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
  def 							lowercase_     (						self						)				->					Dict:
     a   :	str						=    self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'do_resize'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'size'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'do_normalize'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'image_mean'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'image_std'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'do_reduce_labels'						)						)
  def 							lowercase_     (						self						)				->					List[str]:
     a   :	List[Any]						=    self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size  ,					{'height': 30, 'width': 30}						)
     self.assertEqual(image_processor.do_reduce_labels  ,					__UpperCAmelCase						)

     a   :	str						=    self.image_processing_class.from_dict(self.image_processor_dict  ,					size=42  ,					reduce_labels=__UpperCAmelCase						)
     self.assertEqual(image_processor.size  ,					{'height': 42, 'width': 42}						)
     self.assertEqual(image_processor.do_reduce_labels  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					int:
     pass
  def 							lowercase_     (						self						)				->					str:
     a   :	List[str]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     a   :	List[Any]						=    prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__UpperCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase  ,					Image.Image						)

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

     # Test batched
     a   :	Tuple						=    image_processing(__UpperCAmelCase  ,					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 							lowercase_     (						self						)				->					Tuple:
     a   :	str						=    self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     a   :	Dict						=    prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__UpperCAmelCase  ,					numpify=__UpperCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase  ,					np.ndarray						)

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

     # Test batched
     a   :	str						=    image_processing(__UpperCAmelCase  ,					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 							lowercase_     (						self						)				->					int:
     a   :	Optional[Any]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     a   :	Optional[int]						=    prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__UpperCAmelCase  ,					torchify=__UpperCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase  ,					torch.Tensor						)

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

     # Test batched
     a   :	List[str]						=    image_processing(__UpperCAmelCase  ,					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 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[Any]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     a   :	List[str]						=    prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__UpperCAmelCase  ,					torchify=__UpperCAmelCase						)
     a   :	Union[str, Any]						=    []
     for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase  ,					torch.Tensor						)
        maps.append(torch.zeros(image.shape[-2:]						).long()						)

     # Test not batched input
     a   :	Optional[int]						=    image_processing(image_inputs[0]  ,					maps[0]  ,					return_tensors='pt'						)
     self.assertEqual(
         encoding['pixel_values'].shape  ,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(
         encoding['labels'].shape  ,					(
             1,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(encoding['labels'].dtype  ,					torch.long						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 2_55						)

     # Test batched
     a   :	Optional[int]						=    image_processing(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_tensors='pt'						)
     self.assertEqual(
         encoding['pixel_values'].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'],
         )  ,					)
     self.assertEqual(
         encoding['labels'].shape  ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(encoding['labels'].dtype  ,					torch.long						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 2_55						)

     # Test not batched input (PIL images)
     a	, a   :	Dict						=    prepare_semantic_single_inputs()

     a   :	Any						=    image_processing(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_tensors='pt'						)
     self.assertEqual(
         encoding['pixel_values'].shape  ,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(
         encoding['labels'].shape  ,					(
             1,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(encoding['labels'].dtype  ,					torch.long						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 2_55						)

     # Test batched input (PIL images)
     a	, a   :	Union[str, Any]						=    prepare_semantic_batch_inputs()

     a   :	Optional[Any]						=    image_processing(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_tensors='pt'						)
     self.assertEqual(
         encoding['pixel_values'].shape  ,					(
             2,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(
         encoding['labels'].shape  ,					(
             2,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
     self.assertEqual(encoding['labels'].dtype  ,					torch.long						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 2_55						)






  def 							lowercase_     (						self						)				->					Any:
     a   :	List[str]						=    self.image_processing_class(**self.image_processor_dict						)

     # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
     a	, a   :	List[str]						=    prepare_semantic_single_inputs()
     a   :	Union[str, Any]						=    image_processing(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_tensors='pt'						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 1_50						)

     a   :	Tuple						=    True
     a   :	List[Any]						=    image_processing(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_tensors='pt'						)
     self.assertTrue(encoding['labels'].min().item() >= 0						)
     self.assertTrue(encoding['labels'].max().item() <= 2_55						)










def 				A      (	_UpperCAmelCase							:  List[Any]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			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")
							UpperCAmelCase__  =			[int(x) for x in input().split()]  # inputing elements of the list in one line
							UpperCAmelCase__  =			double_sort(lst)
							print("the sorted list is")
							print(sorted_lst)
