code: 
  
  
  
  class        lowerCAmelCase_	:
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	int:
            UpperCamelCase					:						Union[str, Any]					=			data
            UpperCamelCase					:						Optional[Any]					=			None
            UpperCamelCase					:						Any					=			None
  
  
  
  
  
  
         def __str__(    self )  ->	Optional[Any]:
            return F"""{self.data}"""
  
  
  
  
  
  
  class        lowerCAmelCase_	:
         def __init__(    self )  ->	Any:
            UpperCamelCase					:						Tuple					=			None
            UpperCamelCase					:						Union[str, Any]					=			None
         def __iter__(    self )  ->	Optional[int]:
            UpperCamelCase					:						Any					=			self.head
            while node:
               yield node.data
               UpperCamelCase					:						Tuple					=			node.next
         def __str__(    self )  ->	str:
            return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
         def __len__(    self )  ->	List[str]:
            return sum(1 for _ in self )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	List[Any]:
            self.insert_at_nth(0,    SCREAMING_SNAKE_CASE_ )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Optional[Any]:
            self.insert_at_nth(len(self ),    SCREAMING_SNAKE_CASE_ )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Dict:
            UpperCamelCase					:						List[str]					=			len(self )
  
            if not 0 <= index <= length:
               raise IndexError('list index out of range' )
            UpperCamelCase					:						Tuple					=			Node(SCREAMING_SNAKE_CASE_ )
            if self.head is None:
               UpperCamelCase					:						Tuple					=			new_node
            elif index == 0:
               UpperCamelCase					:						Tuple					=			new_node
               UpperCamelCase					:						Optional[Any]					=			self.head
               UpperCamelCase					:						Optional[Any]					=			new_node
            elif index == length:
               UpperCamelCase					:						Optional[int]					=			new_node
               UpperCamelCase					:						Any					=			self.tail
               UpperCamelCase					:						int					=			new_node
            else:
               UpperCamelCase					:						Union[str, Any]					=			self.head
               for _ in range(0,    SCREAMING_SNAKE_CASE_ ):
                  UpperCamelCase					:						Union[str, Any]					=			temp.next
               UpperCamelCase					:						List[Any]					=			new_node
               UpperCamelCase					:						Dict					=			temp.previous
               UpperCamelCase					:						List[str]					=			temp
               UpperCamelCase					:						List[str]					=			new_node
         def       snake_case_ (    self )  ->	Tuple:
            return self.delete_at_nth(0 )
         def       snake_case_ (    self )  ->	Union[str, Any]:
            return self.delete_at_nth(len(self ) - 1 )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	str:
            UpperCamelCase					:						Optional[Any]					=			len(self )
  
            if not 0 <= index <= length - 1:
               raise IndexError('list index out of range' )
            UpperCamelCase					:						Tuple					=			self.head  # default first node
            if length == 1:
               UpperCamelCase					:						Optional[int]					=			None
            elif index == 0:
               UpperCamelCase					:						str					=			self.head.next
               UpperCamelCase					:						List[Any]					=			None
            elif index == length - 1:
               UpperCamelCase					:						Tuple					=			self.tail
               UpperCamelCase					:						Tuple					=			self.tail.previous
               UpperCamelCase					:						int					=			None
            else:
               UpperCamelCase					:						Union[str, Any]					=			self.head
               for _ in range(0,    SCREAMING_SNAKE_CASE_ ):
                  UpperCamelCase					:						Any					=			temp.next
               UpperCamelCase					:						Dict					=			temp
               UpperCamelCase					:						str					=			temp.previous
               UpperCamelCase					:						List[Any]					=			temp.next
            return delete_node.data
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	str:
            UpperCamelCase					:						Dict					=			self.head
  
            while current.data != data:  # Find the position to delete
               if current.next:
                  UpperCamelCase					:						List[str]					=			current.next
               else:  # We have reached the end an no value matches
                  raise ValueError('No data matching given value' )
  
            if current == self.head:
               self.delete_head()
  
            elif current == self.tail:
               self.delete_tail()
  
            else:  # Before: 1 <--> 2(current) <--> 3
               UpperCamelCase					:						Optional[int]					=			current.next  # 1 --> 3
               UpperCamelCase					:						Any					=			current.previous  # 1 <--> 3
            return data
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	List[str]:
            return len(self ) == 0
  
  
  
  
  
  
  def 	UpperCamelCase			(     )			->  None:
     UpperCamelCase					:						Any					=			DoublyLinkedList()
     assert linked_list.is_empty() is True
     assert str(snake_case__			) == ""
  
     try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
     except IndexError:
        assert True  # This should happen.
  
     try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
     except IndexError:
        assert True  # This should happen.
  
     for i in range(10			):
        assert len(snake_case__			) == i
        linked_list.insert_at_nth(snake_case__ ,  i + 1			)
     assert str(snake_case__			) == "->".join(str(snake_case__			) for i in range(1 ,  11			)			)
  
     linked_list.insert_at_head(0			)
     linked_list.insert_at_tail(11			)
     assert str(snake_case__			) == "->".join(str(snake_case__			) for i in range(0 ,  12			)			)
  
     assert linked_list.delete_head() == 0
     assert linked_list.delete_at_nth(9			) == 10
     assert linked_list.delete_tail() == 11
     assert len(snake_case__			) == 9
     assert str(snake_case__			) == "->".join(str(snake_case__			) for i in range(1 ,  10			)			)
  
  
  if __name__ == "__main__":
      from doctest import testmod
  
      testmod()
  
  
  
  
style_context: 
  
  
  
  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 ViltImageProcessor
  
  
  
  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_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
            UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
            UpperCamelCase					:						Dict					=			parent
            UpperCamelCase					:						str					=			batch_size
            UpperCamelCase					:						str					=			num_channels
            UpperCamelCase					:						Optional[int]					=			image_size
            UpperCamelCase					:						List[Any]					=			min_resolution
            UpperCamelCase					:						Tuple					=			max_resolution
            UpperCamelCase					:						Tuple					=			do_resize
            UpperCamelCase					:						Any					=			size
            UpperCamelCase					:						int					=			size_divisor
            UpperCamelCase					:						str					=			do_normalize
            UpperCamelCase					:						Optional[int]					=			image_mean
            UpperCamelCase					:						List[str]					=			image_std
         def       snake_case_ (    self )  ->	List[Any]:
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "size_divisor": self.size_divisor,
            }
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
            if not batched:
               UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
               UpperCamelCase					:						int					=			image_inputs[0]
               if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
               UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
               if h < w:
                  UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size
  
               UpperCamelCase					:						int					=			int((1333 / 800) * size )
               if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                  UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						List[Any]					=			newh * scale
                  UpperCamelCase					:						Optional[int]					=			neww * scale
  
               UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
               UpperCamelCase			,  UpperCamelCase					:						int					=			(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )
  
            else:
               UpperCamelCase					:						Dict					=			[]
               for image in image_inputs:
                  UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
  
            return expected_height, expected_width
  
  
  
  
  
  
  @require_torch
  @require_vision
  class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
         UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return self.image_processor_tester.prepare_image_processor_dict()
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
         def       snake_case_ (    self )  ->	List[Any]:
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size,    {'shortest_edge': 30} )
  
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
            self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
         def       snake_case_ (    self )  ->	Dict:
            pass
         def       snake_case_ (    self )  ->	str:
            # Initialize image_processing
            UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
            for image in image_inputs:
               self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )
  
            # Test not batched input
            UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
         def       snake_case_ (    self )  ->	List[str]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    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					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	Optional[Any]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    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					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  