code: 
  
  
  
  
  """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
  
  
  if is_torch_available():
    import torch
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import SwinaSRImageProcessor
    from transformers.image_transforms import get_image_size
  class 							lowerCamelCase		( unittest.TestCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=7    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=18    ,      SCREAMING_SNAKE_CASE_=30    ,      SCREAMING_SNAKE_CASE_=400    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=1 / 255    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=8    ,      ):
         UpperCamelCase				:   Optional[Any]          =							parent
         UpperCamelCase				:   Optional[int]          =							batch_size
         UpperCamelCase				:   List[Any]          =							num_channels
         UpperCamelCase				:   Any          =							image_size
         UpperCamelCase				:   List[str]          =							min_resolution
         UpperCamelCase				:   int          =							max_resolution
         UpperCamelCase				:   Tuple          =							do_rescale
         UpperCamelCase				:   int          =							rescale_factor
         UpperCamelCase				:   str          =							do_pad
         UpperCamelCase				:   Union[str, Any]          =							pad_size
  
  
  
  
  
  
  
     def       a_							(							self ):
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False ):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
         if equal_resolution:
             UpperCamelCase				:   str          =							[]
             for i in range(self.batch_size ):
                 image_inputs.append(
                     np.random.randint(
                         255    ,      size=(self.num_channels, self.max_resolution, self.max_resolution)    ,      dtype=np.uinta ) )
         else:
             UpperCamelCase				:   str          =							[]
             for i in range(self.batch_size ):
                 UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							np.random.choice(np.arange(self.min_resolution    ,      self.max_resolution )    ,      2 )
                 image_inputs.append(np.random.randint(255    ,      size=(self.num_channels, width, height)    ,      dtype=np.uinta ) )
  
         if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             UpperCamelCase				:   Union[str, Any]          =							[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,      0    ,      -1 ) ) for x in image_inputs]
  
         if torchify:
             UpperCamelCase				:   Any          =							[torch.from_numpy(SCREAMING_SNAKE_CASE_ ) for x in image_inputs]
  
         return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 							lowerCamelCase		( lowercase_	,  unittest.TestCase				):
     lowercase       :  Optional[Any]      =				SwinaSRImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   int          =							SwinaSRImageProcessingTester(self )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,      """do_rescale""" ) )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,      """rescale_factor""" ) )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,      """do_pad""" ) )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,      """pad_size""" ) )
  
  
  
  
  
  
  
     def       a_							(							self ):
         pass
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							get_image_size(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Union[str, Any]          =							self.image_processor_tester.pad_size
  
         UpperCamelCase				:   Tuple          =							(old_height // size + 1) * size - old_height
         UpperCamelCase				:   List[str]          =							(old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Tuple          =							self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCamelCase				:   Optional[int]          =							self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,      Image.Image )
  
         # Test not batched input
         UpperCamelCase				:   List[Any]          =							image_processor(image_inputs[0]    ,      return_tensors="""pt""" ).pixel_values
         UpperCamelCase       ,							UpperCamelCase				:   int          =							self.calculate_expected_size(np.array(image_inputs[0] ) )
         self.assertEqual(
             encoded_images.shape    ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCamelCase				:   Union[str, Any]          =							self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_    ,      numpify=SCREAMING_SNAKE_CASE_ )
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,      np.ndarray )
  
         # Test not batched input
         UpperCamelCase				:   List[str]          =							image_processor(image_inputs[0]    ,      return_tensors="""pt""" ).pixel_values
         UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							self.calculate_expected_size(image_inputs[0] )
         self.assertEqual(
             encoded_images.shape    ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   int          =							self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCamelCase				:   Union[str, Any]          =							self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_    ,      torchify=SCREAMING_SNAKE_CASE_ )
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor )
  
         # Test not batched input
         UpperCamelCase				:   Union[str, Any]          =							image_processor(image_inputs[0]    ,      return_tensors="""pt""" ).pixel_values
         UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							self.calculate_expected_size(image_inputs[0] )
         self.assertEqual(
             encoded_images.shape    ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,      )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
         UpperCamelCase				:   Optional[Any]          =							data
         UpperCamelCase				:   str          =							None
  
  
  
  
  
  
  
     def __repr__(							self ):
         UpperCamelCase				:   str          =							""""""
         UpperCamelCase				:   Any          =							self
         while temp:
             string_rep += f'<{temp.data}> ---> '
             UpperCamelCase				:   Optional[int]          =							temp.next
         string_rep += "<END>"
         return string_rep
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
      # if elements_list is empty
      if not elements_list:
          raise Exception("""The Elements List is empty"""    )
  
      # Set first element as Head
      UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
      UpperCamelCase				:   str          =							head
      # Loop through elements from position 1
      for data in elements_list[1:]:
          UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
          UpperCamelCase				:   Optional[Any]          =							current.next
      return head
  
  
  __A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
  print(F'''List: {list_data}''')
  print('''Creating Linked List from List.''')
  __A :		int      							=    make_linked_list(list_data)
  print('''Linked List:''')
  
  
  print(linked_list)
  
  
  
  
  