




from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__a			:	Union[str, Any]							     =		TypeVar("""T""")
__a			:	Tuple							     =		TypeVar("""U""")




class     A					(				Generic[T, U]       ):







							def __init__(    self   :	Optional[int]   ,     __UpperCAmelCase   :	T | None   ,     __UpperCAmelCase   :	U | None				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_     				= key
										UpperCamelCase_     				= val
										UpperCamelCase_     				= 0
										UpperCamelCase_     				= None
										UpperCamelCase_     				= None







							def __repr__(    self   :	Union[str, Any]				)		->					str:

										"""simple docstring"""



										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     A					(				Generic[T, U]       ):







							def __init__(    self   :	List[str]				)		->					None:

										"""simple docstring"""



										UpperCamelCase_     				= DoubleLinkedListNode(__UpperCAmelCase   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= DoubleLinkedListNode(__UpperCAmelCase   ,     __UpperCAmelCase				)
										UpperCamelCase_				,   UpperCamelCase_     				= self.rear, self.head







							def __repr__(    self   :	Optional[Any]				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= ['DoubleLinkedList']
										UpperCamelCase_     				= self.head
										while node.next is not None:
													rep.append(str(__UpperCAmelCase				)				)
													UpperCamelCase_     				= node.next
										rep.append(str(self.rear				)				)
										return ",\n    ".join(__UpperCAmelCase				)







							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	DoubleLinkedListNode[T, U]				)		->					None:

										"""simple docstring"""



										UpperCamelCase_     				= self.rear.prev

										# All nodes other than self.head are guaranteed to have non-None previous
										assert previous is not None

										UpperCamelCase_     				= node
										UpperCamelCase_     				= previous
										UpperCamelCase_     				= node
										UpperCamelCase_     				= self.rear
										node.freq += 1
										self._position_node(__UpperCAmelCase				)







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	DoubleLinkedListNode[T, U]				)		->					None:

										"""simple docstring"""



										while node.prev is not None and node.prev.freq > node.freq:
													# swap node with previous node
													UpperCamelCase_     				= node.prev

													UpperCamelCase_     				= previous_node.prev
													UpperCamelCase_     				= node.prev
													UpperCamelCase_     				= previous_node
													UpperCamelCase_     				= node







							def        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	DoubleLinkedListNode[T, U]				)		->					DoubleLinkedListNode[T, U] | None:

										"""simple docstring"""



										if node.prev is None or node.next is None:
													return None

										UpperCamelCase_     				= node.next
										UpperCamelCase_     				= node.prev
										UpperCamelCase_     				= None
										UpperCamelCase_     				= None
										return node







class     A					(				Generic[T, U]       ):
							_SCREAMING_SNAKE_CASE  :		dict[Callable[[T], U], LFUCache[T, U]]			  =       {}







							def __init__(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	int				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= DoubleLinkedList()
										UpperCamelCase_     				= capacity
										UpperCamelCase_     				= 0
										UpperCamelCase_     				= 0
										UpperCamelCase_     				= 0
										UpperCamelCase_     				= {}







							def __repr__(    self   :	str				)		->					str:

										"""simple docstring"""



										return (
										    f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
										    f'''capacity={self.capacity}, current_size={self.num_keys})'''
										)







							def __contains__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	T				)		->					bool:

										"""simple docstring"""



										return key in self.cache







							def        lowercase__							(    self   :	Tuple   ,     __UpperCAmelCase   :	T				)		->					U | None:

										"""simple docstring"""



										if key in self.cache:
													self.hits += 1
													UpperCamelCase_     				= self.cache[key]
													UpperCamelCase_     				= 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(__UpperCAmelCase				)
													return node.val
										self.miss += 1
										return None







							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	T   ,     __UpperCAmelCase   :	U				)		->					None:

										"""simple docstring"""



										if key not in self.cache:
													if self.num_keys >= self.capacity:
																# delete first node when over capacity
																UpperCamelCase_     				= 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(__UpperCAmelCase				) is not None
																# first_node guaranteed to be in list

																del self.cache[first_node.key]
																self.num_keys -= 1
													UpperCamelCase_     				= DoubleLinkedListNode(__UpperCAmelCase   ,     __UpperCAmelCase				)
													self.list.add(self.cache[key]				)
													self.num_keys += 1

										else:
													UpperCamelCase_     				= self.list.remove(self.cache[key]				)
													assert node is not None  # node guaranteed to be in list
													UpperCamelCase_     				= value
													self.list.add(__UpperCAmelCase				)







							@classmethod
							def        lowercase__							(    cls   :	type[LFUCache[T, U]]   ,     __UpperCAmelCase   :	int = 128				)		->					Callable[[Callable[[T], U]], Callable[..., U]]:

										"""simple docstring"""



										def cache_decorator_inner(__UpperCAmelCase   :	Callable[[T], U]				) -> Callable[..., U]:
													def cache_decorator_wrapper(*__UpperCAmelCase   :	T				) -> U:
																if func not in cls.decorator_function_to_instance_map:
																			UpperCamelCase_     				= LFUCache(__UpperCAmelCase				)

																UpperCamelCase_     				= cls.decorator_function_to_instance_map[func].get(args[0]				)
																if result is None:
																			UpperCamelCase_     				= func(*__UpperCAmelCase				)
																			cls.decorator_function_to_instance_map[func].put(args[0]   ,     __UpperCAmelCase				)
																return result

													def cache_info() -> LFUCache[T, U]:
																return cls.decorator_function_to_instance_map[func]

													setattr(__UpperCAmelCase   ,     'cache_info'   ,     __UpperCAmelCase				)  # noqa: B010

													return cache_decorator_wrapper

										return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()






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 CLIPImageProcessor




class     A					(				unittest.TestCase       ):







							def __init__(    self   :	Any   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	List[Any]=7   ,     __UpperCAmelCase   :	Union[str, Any]=3   ,     __UpperCAmelCase   :	Optional[Any]=18   ,     __UpperCAmelCase   :	List[str]=30   ,     __UpperCAmelCase   :	int=400   ,     __UpperCAmelCase   :	List[Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     __UpperCAmelCase   :	List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     __UpperCAmelCase   :	Dict=True   ,     )		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= size if size is not None else {'shortest_edge': 20}
										UpperCamelCase_     				= crop_size if crop_size is not None else {'height': 18, 'width': 18}
										UpperCamelCase_     				= parent
										UpperCamelCase_     				= batch_size
										UpperCamelCase_     				= num_channels
										UpperCamelCase_     				= image_size
										UpperCamelCase_     				= min_resolution
										UpperCamelCase_     				= max_resolution
										UpperCamelCase_     				= do_resize
										UpperCamelCase_     				= size
										UpperCamelCase_     				= do_center_crop
										UpperCamelCase_     				= crop_size
										UpperCamelCase_     				= do_normalize
										UpperCamelCase_     				= image_mean
										UpperCamelCase_     				= image_std
										UpperCamelCase_     				= do_convert_rgb







							def        lowercase__							(    self   :	Tuple				)		->					List[Any]:

										"""simple docstring"""



										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	str=False   ,     __UpperCAmelCase   :	Tuple=False   ,     __UpperCAmelCase   :	Optional[int]=False				)		->					Any:

										"""simple docstring"""



										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
													UpperCamelCase_     				= []
													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_     				= []
													for i in range(self.batch_size				):
																UpperCamelCase_				,   UpperCamelCase_     				= 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_     				= [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,     0   ,     -1				)				) for x in image_inputs]

										if torchify:
													UpperCamelCase_     				= [torch.from_numpy(__UpperCAmelCase				) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		Any			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self				)







							@property
							def        lowercase__							(    self   :	Optional[int]				)		->					Optional[Any]:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Tuple				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 20}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 18, 'width': 18}				)

										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42   ,     crop_size=84				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 42}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 84, 'width': 84}				)







							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[int]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	int				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







							def        lowercase__							(    self   :	Optional[Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     numpify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     np.ndarray				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







							def        lowercase__							(    self   :	Optional[Any]				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     torchify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     torch.Tensor				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	str				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self   ,     num_channels=4				)
										UpperCamelCase_     				= 3







							@property
							def        lowercase__							(    self   :	Dict				)		->					str:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Optional[int]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	int				)		->					Any:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	Any				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )
