code: 
  
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  __UpperCAmelCase      = TypeVar('''T''')
  __UpperCAmelCase      = TypeVar('''U''')
  
  
  
  class        lowerCAmelCase_	(						Generic[T, U]							):
         def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Any:
            UpperCamelCase					:						Dict					=			key
            UpperCamelCase					:						List[str]					=			val
            UpperCamelCase					:						Tuple					=			0
            UpperCamelCase					:						List[str]					=			None
            UpperCamelCase					:						Optional[int]					=			None
  
  
  
  
  
  
         def __repr__(    self )  ->	str:
            return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
                self.key,    self.val,    self.freq,    self.next is not None,    self.prev is not None )
  
  
  
  
  
  
  class        lowerCAmelCase_	(						Generic[T, U]							):
         def __init__(    self )  ->	None:
            UpperCamelCase					:						Dict					=			DoubleLinkedListNode(a_,    a_ )
            UpperCamelCase					:						Any					=			DoubleLinkedListNode(a_,    a_ )
            UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.rear, self.head
         def __repr__(    self )  ->	str:
            UpperCamelCase					:						Any					=			['DoubleLinkedList']
            UpperCamelCase					:						List[Any]					=			self.head
            while node.next is not None:
               rep.append(str(a_ ) )
               UpperCamelCase					:						str					=			node.next
            rep.append(str(self.rear ) )
            return ",\n    ".join(a_ )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	None:
            UpperCamelCase					:						int					=			self.rear.prev
  
            # All nodes other than self.head are guaranteed to have non-None previous
            assert previous is not None
  
            UpperCamelCase					:						List[str]					=			node
            UpperCamelCase					:						int					=			previous
            UpperCamelCase					:						str					=			node
            UpperCamelCase					:						Tuple					=			self.rear
            node.freq += 1
            self._position_node(a_ )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	None:
  
            while node.prev is not None and node.prev.freq > node.freq:
               # swap node with previous node
               UpperCamelCase					:						Union[str, Any]					=			node.prev
  
               UpperCamelCase					:						Optional[Any]					=			previous_node.prev
               UpperCamelCase					:						Optional[Any]					=			node.prev
               UpperCamelCase					:						Optional[int]					=			previous_node
               UpperCamelCase					:						List[Any]					=			node
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	DoubleLinkedListNode[T, U] | None:
  
            if node.prev is None or node.next is None:
               return None
  
            UpperCamelCase					:						Any					=			node.next
            UpperCamelCase					:						Dict					=			node.prev
            UpperCamelCase					:						List[Any]					=			None
            UpperCamelCase					:						List[Any]					=			None
            return node
  
  
  
  
  
  
  class        lowerCAmelCase_	(						Generic[T, U]							):
         UpperCAmelCase__       :      dict[Callable[[T], U], LFUCache[T, U]]       =						{}
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[Any]:
            UpperCamelCase					:						str					=			DoubleLinkedList()
            UpperCamelCase					:						Tuple					=			capacity
            UpperCamelCase					:						Optional[Any]					=			0
            UpperCamelCase					:						int					=			0
            UpperCamelCase					:						Optional[Any]					=			0
            UpperCamelCase					:						Optional[int]					=			{}
         def __repr__(    self )  ->	str:
            return (
                F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
                F"""capacity={self.capacity}, current_size={self.num_keys})"""
            )
         def __contains__(    self,    SCREAMING_SNAKE_CASE_ )  ->	bool:
            return key in self.cache
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	U | None:
  
            if key in self.cache:
               self.hits += 1
               UpperCamelCase					:						List[str]					=			self.cache[key]
               UpperCamelCase					:						Dict					=			self.list.remove(self.cache[key] )
               assert node == value_node
  
               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(a_ )
               return node.val
            self.miss += 1
            return None
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	None:
  
            if key not in self.cache:
               if self.num_keys >= self.capacity:
                  # delete first node when over capacity
                  UpperCamelCase					:						Any					=			self.list.head.next
  
                  # guaranteed to have a non-None first node when num_keys > 0
                  # explain to type checker via assertions
                  assert first_node is not None
                  assert first_node.key is not None
                  assert self.list.remove(a_ ) is not None
                  # first_node guaranteed to be in list
  
                  del self.cache[first_node.key]
                  self.num_keys -= 1
               UpperCamelCase					:						Any					=			DoubleLinkedListNode(a_,    a_ )
               self.list.add(self.cache[key] )
               self.num_keys += 1
  
            else:
               UpperCamelCase					:						List[Any]					=			self.list.remove(self.cache[key] )
               assert node is not None  # node guaranteed to be in list
               UpperCamelCase					:						str					=			value
               self.list.add(a_ )
  
  
  
  
  
  
         @classmethod
         def       snake_case_ (    cls,    SCREAMING_SNAKE_CASE_ = 128 )  ->	Callable[[Callable[[T], U]], Callable[..., U]]:
  
            def cache_decorator_inner(SCREAMING_SNAKE_CASE_ ) -> Callable[..., U]:
               def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE_ ) -> U:
                  if func not in cls.decorator_function_to_instance_map:
                     UpperCamelCase					:						Any					=			LFUCache(a_ )
  
                  UpperCamelCase					:						Dict					=			cls.decorator_function_to_instance_map[func].get(args[0] )
                  if result is None:
                     UpperCamelCase					:						Optional[Any]					=			func(*a_ )
                     cls.decorator_function_to_instance_map[func].put(args[0],    a_ )
                  return result
  
               def cache_info() -> LFUCache[T, U]:
                  return cls.decorator_function_to_instance_map[func]
  
               setattr(a_,    'cache_info',    a_ )  # noqa: B010
  
               return cache_decorator_wrapper
  
            return cache_decorator_inner
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.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,
                ),    )
  
  
  
  