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, prepare_image_inputs
  
  
  if is_torch_available():
    import torch
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import ConvNextImageProcessor
  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_=None    ,      SCREAMING_SNAKE_CASE_=0.875    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,      ):
         UpperCamelCase				:   Tuple          =							size if size is not None else {'''shortest_edge''': 20}
         UpperCamelCase				:   List[Any]          =							parent
         UpperCamelCase				:   Optional[int]          =							batch_size
         UpperCamelCase				:   str          =							num_channels
         UpperCamelCase				:   Union[str, Any]          =							image_size
         UpperCamelCase				:   Optional[Any]          =							min_resolution
         UpperCamelCase				:   Union[str, Any]          =							max_resolution
         UpperCamelCase				:   Tuple          =							do_resize
         UpperCamelCase				:   List[Any]          =							size
         UpperCamelCase				:   Optional[int]          =							crop_pct
         UpperCamelCase				:   Any          =							do_normalize
         UpperCamelCase				:   Optional[int]          =							image_mean
         UpperCamelCase				:   Union[str, Any]          =							image_std
  
  
  
  
  
  
  
     def       a_							(							self ):
         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 							lowerCamelCase		( __UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  List[Any]      =				ConvNextImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							ConvNextImageProcessingTester(self )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(lowercase_    ,      """do_resize""" ) )
         self.assertTrue(hasattr(lowercase_    ,      """size""" ) )
         self.assertTrue(hasattr(lowercase_    ,      """crop_pct""" ) )
         self.assertTrue(hasattr(lowercase_    ,      """do_normalize""" ) )
         self.assertTrue(hasattr(lowercase_    ,      """image_mean""" ) )
         self.assertTrue(hasattr(lowercase_    ,      """image_std""" ) )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   int          =							self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size    ,      {"""shortest_edge""": 20} )
  
         UpperCamelCase				:   Tuple          =							self.image_processing_class.from_dict(self.image_processor_dict    ,      size=42 )
         self.assertEqual(image_processor.size    ,      {"""shortest_edge""": 42} )
  
  
  
  
  
  
  
     def       a_							(							self ):
         pass
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Tuple          =							self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCamelCase				:   List[str]          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_ )
         for image in image_inputs:
             self.assertIsInstance(lowercase_    ,      Image.Image )
  
         # 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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,      )
  
         # Test batched
         UpperCamelCase				:   int          =							image_processing(lowercase_    ,      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       a_							(							self ):
         UpperCamelCase				:   Tuple          =							self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCamelCase				:   Tuple          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_    ,      numpify=lowercase_ )
         for image in image_inputs:
             self.assertIsInstance(lowercase_    ,      np.ndarray )
  
         # 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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,      )
  
         # Test batched
         UpperCamelCase				:   Union[str, Any]          =							image_processing(lowercase_    ,      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       a_							(							self ):
         UpperCamelCase				:   Union[str, Any]          =							self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCamelCase				:   Dict          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_    ,      torchify=lowercase_ )
         for image in image_inputs:
             self.assertIsInstance(lowercase_    ,      torch.Tensor )
  
         # Test not batched input
         UpperCamelCase				:   Union[str, 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
         UpperCamelCase				:   List[Any]          =							image_processing(lowercase_    ,      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"""],
             )    ,      )
  
  
  
  
  
  
  
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)
  
  
  
  
  